# Entry

Writing to files is an indispensable Python skill. It allows you to save data permanently instead of losing it when you stop the program. You can utilize file saving to store results, logs, reports, user input, settings, and structured data.

In this guide, you’ll learn how to create text files, write multiple lines, attach content, work with folders, and save data in CSV and JSON formats. You’ll also learn about the most popular file modes, including w, a, xAND rand when to utilize each one.

By the end, you will be able to write Python programs that write results, reports, logs, and structured data to files.

# Saving the first text file

The easiest way to write to a file is to utilize the built-in Python language open() function.

The w mode means recording mode. If the file does not exist, Python creates it. If the file already exists, Python replaces its existing contents.

file = open("message.txt", "w")
file.write("Hello, this is my first file written with Python.")
file.close()

When you run this code, Python creates a file called message.txt in the same folder where the notebook or script is located.

You can re-read the file to see what was written.

file = open("message.txt", "r")
content = file.read()
file.close()

print(content)

Exit:

Hello, this is my first file written with Python.

# Using with open(): A better way

Although you can manually open and close files, the recommended approach is to utilize with open().

This will automatically close the file when the code block ends. It is cleaner, safer, and widely used in real Python projects.

with open("message.txt", "w") as file:
    file.write("This file was written using with open().")

with open("message.txt", "r") as file:
    content = file.read()

print(content)

Exit:

This file was written using with open().

Using with open() this is best practice because you don’t have to remember to close the file manually.

# Understanding file modes

When you open a file, mode tells Python what you want to do with it.

For saving files, the most common modes are w AND a. Employ w when you want to create a recent file or replace existing content. Employ a when you want to add recent content at the end of the file.

# Writing multiple lines

You can write multiple lines by adding a newline character n.

with open("notes.txt", "w") as file:
    file.write("Line 1: Learn Pythonn")
    file.write("Line 2: Practice file handlingn")
    file.write("Line 3: Build small projectsn")

Read file:

with open("notes.txt", "r") as file:
    print(file.read())

Exit:

Line 1: Learn Python
Line 2: Practice file handling
Line 3: Build compact projects

You can also utilize writelines() save a list of strings to a file.

tasks = [
    "Write Python coden",
    "Run the notebookn",
    "Check the output filen"
]

with open("tasks.txt", "w") as file:
    file.writelines(tasks)

Read file:

with open("tasks.txt", "r") as file:
    print(file.read())

Exit:

Write Python code
Run the notebook
Check the output file

There is one essential thing to remember writelines() does not automatically add line breaks. You must include n myself.

# Appending to a file

Sometimes you don’t want to overwrite the existing content of a file. Instead, you can add recent content at the end.

To do this, utilize append mode: a.

with open("journal.txt", "w") as file:
    file.write("Day 1: I started learning Python file handling.n")

with open("journal.txt", "a") as file:
    file.write("Day 2: I learned how to append text to a file.n")

Read file:

with open("journal.txt", "r") as file:
    print(file.read())

Exit:

Day 1: I started learning Python file handling.
Day 2: I learned how to append text to a file.

Append mode is useful when working with logs, logs, reports, or any file to which you want to add recent information.

# Secure file creation

If you want to create a recent file but avoid overwriting the existing one, utilize x mode.

This mode only creates a file if it does not already exist. If the file already exists, Python calls a FileExistsError.

try:
    with open("new_file.txt", "x") as file:
        file.write("This file was created using x mode.")
    print("File created successfully.")
except FileExistsError:
    print("The file already exists, so Python did not overwrite it.")

If the file does not exist, you may see:

File created successfully.

If the file already exists, you can see:

The file already exists, so Python did not overwrite it.

This is useful when you want to protect existing files from being accidentally overwritten.

# Working with file paths

By default, Python saves files to the same folder where your notebook or script runs.

If you want to save files to a specific folder, you can utilize library_path.

from pathlib import Path

output_folder = Path("output")
output_folder.mkdir(exist_ok=True)

file_path = output_folder / "summary.txt"

with open(file_path, "w") as file:
    file.write("This file was saved inside the output folder.")

print(f"File saved to: {file_path}")

Exit:

File saved to: output/summary.txt

Now read the file:

with open("output/summary.txt", "r") as file:
    print(file.read())

Exit:

This file was saved inside the output folder.

The mkdir(exist_ok=True) call creates a folder if it doesn’t already exist. If the folder already exists, Python does not report an error.

# Saving CSV files

CSV files are useful for saving tabular data such as rows and columns. They are commonly opened in spreadsheet tools such as Excel or Google Sheets.

To write a CSV file in Python, utilize the method csv module.

import csv

students = [
    ["Name", "Score"],
    ["Ayesha", 92],
    ["Bilal", 85],
    ["Sara", 88]
]

with open("students.csv", "w", newline="") as file:
    writer = csv.writer(file)
    writer.writerows(students)

Read the CSV file:

with open("students.csv", "r") as file:
    print(file.read())

Exit:

Name,Score
Ayesha,92
Bilal,85
Sara,88

The newline="" The argument helps avoid extra blank lines when saving CSV files, especially on Windows.

# Saving JSON files

JSON is another common format for storing structured data. It is often used for dictionaries, API responses, configuration files, and nested data.

To write JSON files in Python, utilize the json module.

import json

profile = {
    "name": "Ayesha",
    "role": "Data Analyst",
    "skills": ["Python", "SQL", "Excel"],
    "active": True
}

with open("profile.json", "w") as file:
    json.dump(profile, file, indent=4)

Read the JSON file:

with open("profile.json", "r") as file:
    print(file.read())

Exit:

{
    "name": "Ayesha",
    "role": "Data Analyst",
    "skills": [
        "Python",
        "SQL",
        "Excel"
    ],
    "active": true
}

The indent=4 argument makes the JSON file easier to read.

# Common beginner mistakes

Here are some common mistakes beginners make when writing files in Python.

# Summary

Writing to files is one of the most useful Python skills for beginners. I still remember entering a programming competition in my second semester of engineering and wasting almost an hour trying to figure out how to save the file. If I had known it was that plain, I could have won.

File saving helps you store logs, save program output, create reports, store user data, and even read and write plain databases using formats like JSON. The best part is that Python file handling is native, speedy, and works out of the box.

For most tasks, utilize with open() because it automatically closes the file. Employ w save or overwrite the file, a to add recent content and x to safely create a recent file without overwriting the existing one.

Abid Ali Awan (@1abidaliawan) is a certified data science professional who loves building machine learning models. Currently, he focuses on creating content and writing technical blogs about machine learning and data science technologies. Abid holds a Master’s degree in Technology Management and a Bachelor’s degree in Telecommunications Engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.

We have idea of ​​the solar system’s past: it was full of violence and chaos. However, we are still investigating how brutal this event was. Current models suggest that at some point after the formation of the giant planets, they underwent a phase of such extreme instability that one or even two bodies the size of Uranus or Neptune were ejected into interstellar space. If such a scenario comes true, we could find clues in the most unexpected places in the solar system, such as the moons of Jupiter and especially Uranus.

A recent article published in Icarus analyzed 122 possible scenarios for such instability to assess how the satellite systems of “left behind” planets would respond. The scientists concluded that it would be extremely arduous to explain the current characteristics of Uranus’s moons without some episode of violent instability. This type of instability only appears in models where there were more giant planets than we see today.

The authors indicate that Uranus’s moons were most likely destabilized at least twice in the past: first by an impact that tilted the planet, and then by a close encounter between the giant planets during a time of instability. This chaos, fueled by the presence of one or more planets that were later ejected, would destroy and rebuild the moon system to the state we see today.

The solar system and chaos

Jupiter, Saturn, Uranus and Neptune have not always had their current positions in the solar system. According to the planetary instability model, they were born slightly closer to the Sun and closer to each other. After millions of years, they migrated towards their current orbits.

However, there are details of this model that do not agree with observations. First, the current orbits of Jupiter and Saturn are eccentric, while there are specific structures, such as the Kuiper Belt, that apparently should have prevented Neptune from moving to its current position. In the simulations, the planets did not get to where they are today.

It is therefore possible that the Solar System at one point had more planets and they “pushed out the others.” According to this hypothesis, the puzzle of the solar system fits better. The problem is that these bodies, if they existed, are gone – they were thrown away and left no physical traces or fragments. This leaves the concept of lost planets as a hypothesis awaiting sufficient evidence to be confirmed.

Extraordinary Moon

Modern Icarus the study tested the missing planets hypothesis using Uranus’s moons as direct evidence. A total of 122 simulations of the evolution of the Solar System were used. In 85 percent of scenarios, the Uranus lunar system collapses. In only a few scenarios did its moons survive, and in all of them the lost and ejected planets hypothesis fit very well.

The report points to Miranda, the smallest moon in the main Uranus system. Astronomers believe it is the most unusual object in the solar system. It’s patchy, as if sewn together from scraps, too icy for its size, and quite miniature compared to Uranus’ other moons. It is also geologically vigorous.

Astronomers believe Miranda is the remnant of a larger body. The study confirms this thesis and suggests that this is the clearest example of traces of planetary instability.

# Entry

A model that claims to be 90% confident should be right 90% of the time. When this relationship falls apart, you will receive incorrect calibration problem. The model results no longer say anything useful about reliability.

For enormous language models (LLM) miscalibration is common. AND NAACL 2024 Study found that confidence scores deviated from actual correctness rates in QA, code generation, and inference tasks.

Other test biomedical models found average calibration scores ranging from just 23.9% to 46.6% across all models tested. The difference is constant.

Standard solution in classic machine learning is post-hoc recalibration: fit a uncomplicated function to the set aside validation set to map the raw confidence scores to better calibrated probabilities.

Three dominant methods: temperature scaling, Platt scalingAND isotonic regression. All three were designed for discriminative classifiersand applying them to LLM requires caution.

# Measurement calibration

The dominant metric is Expected calibration error (ECG). Groups predictions into confidence intervals, calculates the difference between mean confidence and observed accuracy within each interval, and averages across intervals weighted by size. ECE = 0 is the perfect calibration.

A reliability diagram shows the relationship between confidence and accuracy. A perfectly calibrated model sits on a diagonal. Below is the overconfidence model: The curve shows high confidence, but accuracy cannot keep up.

AND Rating 2025 GPT-4o-mini as a text classifier found that 66.7% of errors occurred at confidence levels above 80% – the canonical pattern of overconfidence.

ECE alone is increasingly seen as insufficient. AND research article recommends pairing ECE with Brier scoreoverconfidence factors and reliability diagrams combined. A single number obscures significant differences in where and how the model behaves incorrectly.

# Why LLMs complicate the standard setup

The three methods we discuss assume a constant output space. The classifier generates one probability per class, and calibration maps them to better estimates.

LLM don’t act this way.

There are four complications that matter here.

The output space is exponentially enormous: confidence cannot be computed at the sequence level. Semantically equivalent outcomes may have very different token-level probabilities. The trust disagrees with the details; AND research article atomic calibration showed that generative models show the lowest average confidence in the middle of the generation, rather than at the beginning or end.

Many LLMs only reveal the probabilities of the highest-k tokens through their APItherefore, classic calibration approaches that rely on full logit access require modification.

# Applying temperature scaling

Temperature scaling divides the logit vector by the T scalar before applying softmax. When T > 1, the distribution flattens and confidence decreases. When T

T fits the validation set held out by minimizing the negative log-likelihood. The method adds one parameter, preserves prediction rankings, and is economical to compute.

The original formula targeted DenseNet image classifiers. In the case of LLM, temperature controls the probability distribution of the vocabulary at each decoding stage, so the same logic applies.

The problem is this Reinforcement learning from human feedback (RLHF). Post-RLHF models develop input-dependent overconfidence: the degree of miscalibration varies with input, and a single T cannot explain this variation.

Average ECE scores above 0.377 have been documented for models such as GPT-3 on verbalized self-confidence tasks and 2025 study confirms that RLHF-tuned models consistently overestimate confidence in all cases.

Adaptive temperature scaling (ATS) deals with this directly. ATS predicts the temperature for each token based on hidden features at the token level, adapting to a supervised tuning dataset rather than using a single fixed T. Researchers confirmed that ATS improved calibration by 10-50% without compromising task performance. For any RLHF-tuned model, ATS provides a stronger baseline than standard temperature scaling.

Standard temperature scaling still works well for pre-RLHF base models. When miscalibration is approximately uniform across all inputs, a single T is often sufficient to correct for systematic over- or under-confidence.

The problem is specific to post-RLHF models, where input-dependent overconfidence means that a single T cannot correct for all inputs.

# Application of Platt scaling

Platt scaling fits a logistic function based on uncalibrated results: p = σ(A·s + B), where A and B learn from the issued validation set with binary correctness labels.

The sigmoid shape provides a parametric mapping with two free parameters.

Platt scaling was originally developed for SVMs, but can be generalized to any system that produces a scalar confidence score.

Two-parameter fitting also allows for productive operate of data compared to isotonic regression: it can generate useful estimates from a smaller calibration set, which is crucial in implementation contexts where labeled validity data is circumscribed.

In the context of LLM, Platt scaling works on sequence-level or token-level confidence scores.

AND paper based on the code confidence generated by LLM, it was found that Platt scaling produced better calibrated results than uncalibrated results. Another study on LLM for Text to SQL Conversion has been introduced Platt’s multidimensional scaling (MPS), extending single-variable Platt scaling to combine sub-score frequency scores across multiple generated samples – consistently outperforming single-score baselines.

Two limitations are documented. First, global Platt scaling at the sequence level is too abrasive for tasks where correctness depends on local editing decisions: a single sigmoid mapping is unable to capture sample-dependent miscalibration patterns.

Additionally, Platt scaling may degrade correct scoring performance for robust models.

# Using isotonic regression

Isotonic regression is performed using a non-parametric method.

It learns a piecewise constant, monotone, non-decreasing mapping from uncalibrated outcomes to calibrated probabilities using Neighboring pool violators algorithm (PAWA). There is no assumed shape for the calibration function, which makes it more malleable than Platt scaling when the confidence-accuracy relationship is not sigmoidal.

The piecewise constant output adapts to any monotonous shape: linear, stepped or concave. This adaptability is the main reason why isotonic regression tends to outperform Platt scaling in empirical comparisons.

The cost is the risk of overfitting for miniature calibration sets. Mapping generalizes well only when there is enough data to constrain it.

Empirically, isotonic regression outperforms Platt scaling.

Exacting comparison across multiple datasets and architectures showed that isotonic regression outperforms Platt scaling on ECE and Brier scores with statistical significance, using paired tests with a Bonferroni correction at α = 0.003.

In this study, the Random Forest baseline improved from a reliability score of 0.8268 in the uncalibrated condition, to 0.9551 in the Platt scale, and to 0.9660 in the isotonic regression condition. Both methods can degrade actual scoring performance for robust models, but the isotonic advantage persists consistently.

For multiclass LLM settings, it has been shown that standard isotonic regression can be further improved with extensions supporting normalization, consistently outperforming both OvR isotonic regression and standard parametric methods on NLL and ECE.

The data requirement is a binding restriction. The advantage of isotonic regression is real, but it does not translate to low-data deployment scenarios.

# What literature leaves open

Three gaps it is worth marking them before implementing any of these methods.

The RLHF the interaction was only examined in terms of temperature scaling. How Platt scaling and isotonic regression in post-RLHF models has not been systematically tested. ATS exists because standard temperature scaling required an explicit solution in this case. It is an open question whether the other two methods require similar extensions.

The most direct comparisons all three methods are taken from the general machine learning calibration literature. LLM-specific benchmarks that test all three directly are uncommon. ICSE Code Calibration 2025 paper is one of the few and its scope is circumscribed to code generation.

The size of the calibration set is a real implementation limitation. The isotonic regression results from the articles assume that the datasets are enormous enough to limit mapping. In production with a circumscribed number of labeled examples, the gap between isotonic regression and Platt’s scale may close or reverse.

# Application

Temperature scaling is the right starting point for most teams. For entry-level models without RLHF, a single T is often sufficient.

For RLHF-tuned models, switch to ATS: per-token temperature supports input-dependent overconfidence that the global scalar lacks.

Platt scaling is a practical choice when the calibration set is miniature or when calibration requires inclusion in a larger pipeline. It is productive in data processing and uncomplicated to implement. The limitation is scope: it cannot capture miscalibration, which varies from sample to sample and tends to degrade performance for robust models.

Isotonic regression has the strongest empirical track record of the three. Utilize it when the calibration set is enormous enough to constrain the mapping without overfitting, and combine it with extensions that support normalization in multiclass settings.

The decision that comes before all this is what “trust” means for the task. Symbolic probability, sequence probability, verbalized confidence, and consistency across samples can all produce different values ​​for the same result. A calibration method applied to the wrong signal does not improve reliability. The correct definition is a prerequisite for any of the above methods to work.

Nate Rosidi is a data scientist and product strategist. He is also an adjunct professor of analytics and the founder of StrataScratch, a platform that helps data scientists prepare for job interviews using real interview questions from top companies. Nate writes about the latest career trends, gives interview advice, shares data science projects, and discusses all things SQL.

Carnivorous parasite A fly that poses a solemn threat to livestock has returned to the United States after 60 years. This week, the US Department of Agriculture confirmed presence of the Fresh World snail in a calf in south Texas.

Eliminated in the US in 1966 and as far south as possible Panama by 2006, its recent re-emergence in Mexico increased the likelihood that the snail would eventually re-enter the country, and modeling showed it could arrive as early as summer 2025. It took a little longer, but the snail arrived. To prevent an outbreak, officials are using a proven technique: releasing masses of adult snail flies.

A worm infection occurs when a female fly lays eggs in open wounds or other body parts of warm-blooded animals. Once the eggs hatch, worms emerge and feed on living tissue before turning into flies. As adults, snail flies do not bite or feed on flesh. Scientists in the 1930s and 1940s thought that if they could prevent female flies from breeding, they could break the cycle. At the time, Fresh World snails were killing hundreds of thousands of cattle each year, mostly in the American South and Southwest.

In the 1950s, USDA researchers made a breakthrough by administering radiation to male snails and rendering them sterile. Once released into an infected area, sterile males mate with wild female insects and lay nonviable eggs. No offspring are born and the population collapses. Known as the sterile insect technique, it was first successfully used on the island of Curaçao off the coast of Venezuela. It took just seven weeks to eradicate the pest, and the effort saved the island’s goat herds, which were a vital food source.

This technique takes advantage of the fact that female Fresh World snails mate only once in their lives. “The sterile insect technique is perhaps the most telling example of a completely effective biological control mechanism,” says Sally DeNotta, associate professor of veterinary medicine at the University of Florida. “The life cycle stops. No offspring are produced. It was very successful.”

For years, a dense stretch of rainforest between Panama and Colombia known as the Darién Pass served as a biological barrier through which sterile flies were released to prevent the snails from spreading north. However, insects began to break through this barrier in 2022.

To prevent an outbreak in south Texas, the USDA has sealed off an approximately 12-mile area around the infected calf and is conducting targeted releases of sterile snail flies from trucks. This is in addition to the 4 million sterile flies a week already dropped in the area. The agency predicts the snail will move north in February moved its efforts to disperse 100 million sterile flies per week to focus on an area along the U.S.-Mexico border.

“While this development poses a serious threat to our livestock and wildlife, it does not surprise us,” USDA Secretary Brooke Rollins said during a hearing of the House Agriculture Committee meeting on Thursday.

She said it takes about 400 million flies a week to repel the bugs. Currently, only about 100 million flies can be produced per week in the United States facility located in Panama.

A sterile insect facility in Mexico closed in 2012, but the USDA did investing $21 million Assisting in the renovation and conversion of an existing fruit fly facility in Metapa, Mexico to produce an additional 60-100 million sterile flies per week. According to the USDA, the facility is expected to be operational this summer.

Last November, you and everyone else fell down the coding agent rabbit hole?

It was an intense time for nerds. During this vacation, I spent a lot of time talking to programming agents. And since then it has been an absolute rocket ship.

Claude Code seems to have gotten the thunder there, defeating Codex and, frankly, the co-pilot. A few years ago, Microsoft’s Copilot coding tool seemed to be leading the pack. Now it’s Claude Code.

I would respectfully disagree. Coding models are part of it, but Microsoft is a great place for developers. Windows is an open platform on open hardware that people can build anything on.

Microsoft wants Scout to be adopted by production workers and even consumers. AI agents make mistakes and hallucinate. How many mistakes will people tolerate?

That’s a good question. I don’t know. Trust, but verify. Give him a compact task and then try it out and see if it works. And then: “Oh, he didn’t do anything wrong. I’ll give him read-only access to something.” For example, when I tell someone that I gave OpenClaw access to my blood sugar levels because I have type 1 diabetes, the knee-jerk reaction is, “How dare you share your health data with an agent?” Receiving vigorous blood sugar notifications is very useful for me. I don’t think this is controversial.

I understand that, but many people are skeptical or hostile towards artificial intelligence these days.

When a recent tool is introduced to the market, whether it’s a chainsaw, a power tool, or an internal combustion engine, chaos ensues as people wonder how to make the tool good for people. Personally, I am not a supporter of artificial intelligence because I vote with my feet. I don’t employ AI image generation or AI video generation because I don’t believe in such things. I employ artificial intelligence to code and I enjoy it.

Yes, developers absolutely love agents, but there is resistance outside of this community. Microsoft noticed this after the impoverished performance of its AI productivity tools. Do you foresee similar difficulties with agent-based AI?

Either they like it or they don’t. I remember when the Walkman came out and people said, “No one will wear these things on their heads. These headphones look ridiculous.” Now we all walk around with these white Q-tips hanging from our ears.

Don’t you feel like Microsoft is playing catch-up?

I would respectfully step back and point out that everyone is in catch-up mode because you’re pulling forward and then going back and forth. It’s a war of thumbs. I remind people that the term “Copilot” was something Microsoft did first and became a term similar to Kleenex.

Do you think this year’s developer conference has put Microsoft back in the race?

Several Mac users hung out with me behind the scenes and watched Surface Ultra laptop announced. They saw all the recent developer tools, looked at us reluctantly, and said, “Hell, you’re making me buy a Surface, aren’t you?”

Fort Mason’s garbage cans are now full of MacBook Airs?

This would be an amazing result, although I would not like to produce more ecological waste.

This is the release Steven Levy Backchannel newsletter. Read previous newsletters Here.

# Entry

# Step 1: Understand what makes time series data special

The three most critical structural properties are summarized below:

# Step 2: Master time series data structures in Python

The distinction between DatetimeIndex and PeriodIndex is more critical than it initially seems.

• DatetimeIndex represents specific moments in time.
• PeriodIndex represents time intervals.

Knowing when to employ each of them, how to convert between them, and how to parse, cut, and resample time-indexed data can save you a lot of trouble later, as most modeling libraries have their own specific format requirements.

Resampling and aggregation are where many analysts make mute, significant errors. Downsampling from minute to hourly data requires selecting the correct aggregation function, and incorrectly specifying it disrupts the analysis. Practicing resampling with multiple aggregation strategies on the same dataset until the logic becomes intuitive is time well spent.

Roll-up and roll-out windows — .rolling() AND .expanding() — are pandas primitives for latency features and cumulative statistics. Manually building moving averages, standard deviations, and lag offsets before relying on library abstractions is critical: understanding what these operations do at the index level prevents a whole class of subtle data leak errors that are extremely challenging to diagnose after the fact.

Rescue: Work through pandas Guide to time series and date functionality with the actual data set before continuing.

# Step 3: Learning how to pristine and prepare time series data

• Global statistical thresholds may ignore anomalies in non-stationary series.
• Rolling Z-scores and IQR boundaries in sliding windows support detect anomalous values ​​in their local neighborhood.
• For multi-dimensional sensor data Insulating forest detects anomalies that may not appear in individual channels but appear in connected functions.

Rescue: : sktime transformation documentation covers the most common preprocessing transformations with helpful examples.

# Step 4: Developing intuition through exploratory analysis

• Is the trend linear or non-linear?
• Is the seasonal amplitude stable or does it change over time?
• Is the residue approximately white noise, or does it contain structure that the decomposition missed?

Another critical diagnostic is autocorrelation analysis. Autocorrelation function (ACF) and partial autocorrelation function (PACF) plots are imperative tools for understanding time relationships:

• A slowly decaying ACF signals non-stationarity.
• Significant spikes in hourly data with a 24-hour delay signal daily seasonality.
• PACF cutoff values ​​suggest an autoregressive (AR) order.

Fluent reading of these charts is imperative in any classic modeling work.

Stationarity testing complements the exploratory workflow. The Augmented Dickey-Fuller (ADF) test and Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test they provide statistical evidence for or against stationarity, and it is worthwhile to conduct both because they test complementary hypotheses. The results indicate whether differentiation or transformation is needed before modeling.

# Step 5: Construction of classic statistical forecast models

Rescue: : Forecasting: Principles and Practice, Chapters 7–9 for ETS and ARIMA and statsmodels State space documentation for details on the Python-specific implementation.

# Step 6: Move to machine learning and deep learning models

Tree-based models such as Lightweight GBM AND XGBoost generate powerful forecasts by taking into account well-designed lag functions, rolling statistics and calendar variables. They automatically deal with non-linearity and interactions between functions, but the main risk is data leakage; delays must be constructed solely based on past values ​​relative to the prediction timestamp. sktime make_reduction safely wraps scikit-learn regressors as predictors and handles this accounting correctly.

Deep learning architectures have the best track record on benchmark datasets and perform better at multi-season, covariate and long-term forecasting than classical models. NeuralForecast implements all this with a consistent API and appropriate short-lived cross-validation support. The right time to turn to deep learning is after simpler models have stabilized, not before.

Rescue: : Kaggle M5 Forecasting competition notebooks are a good starting point, and the best solutions they cover the entire process from feature engineering to assembly based on a real-world retail forecasting problem and are publicly available.

# Step 7: Implementation and monitoring of forecasting systems

Forecast storage and versioning require thoughtful design. Manufacturing forecasting systems generate forecasts continuously, and storing forecasts along with predicted facts – not just the final model results – allows you to calculate retrospective accuracy over each time horizon and understand exactly where the model is deteriorating over time.

Backtesting as a gateway to implementation is the discipline that separates experiments from production-ready systems. Before any model is implemented, exacting backtesting should simulate the entire implementation window using only data that would be available at each stage. A model that looks good on the exposed test set but doesn’t backtest properly is not ready.

Rescue: : Apparently an AI model monitoring guide for machine learning monitoring, including data drift detection and predictions.

# Summary

Priya C’s girlfriend is a software developer and technical writer from India. He likes working at the intersection of mathematics, programming, data analytics and content creation. Her areas of interest and specialization include DevOps, data analytics and natural language processing. She likes reading, writing, coding and coffee! He is currently working on learning and sharing his knowledge with the developer community by writing tutorials, guides, reviews, and more. Bala also creates fascinating resource overviews and coding tutorials.

Finding a legally eco-friendly one being able to travel is complex, not to mention time-consuming. The gap between sustainability claims and practices can be quite huge, and “eco-crime” is not always straightforward to identify.

But there are signs to look for. Scientists from Turkey recently Five key categories have been identified Describe the most common forms of greenwashing in tourism: eco-certifications, penniless waste management, misleading carbon offset claims, over-consumption by destination, and the exploit of the “green development” label to mask social injustice and environmental damage.

“Companies facing demands for environmental and social responsibility often make gestures that are largely for show” – authors he wrote in the newspaper published in May in the journal Frontiers in Sustainability.

As the newspaper explains, there are many reasons for concern, but there are also ways to cut through the noise. Independent and hearty certification systems play a huge role; local business is also significant, as corporate chains are often associated with problematic greenwashing, especially at the luxury level. “Sustainability cannot be seen as a communication strategy, but as a structural commitment that is measurable, inclusive and ethically embedded,” the authors wrote.

The first thing to remember when planning a trip is that it will have a negative impact. Any company saying they are helping the environment, instead of explaining what they are doing to reduce their footprint, is a giant red flag. It takes a little more effort to see anything beyond that. Take this into account when booking your trip.

Do those little notes asking you to reuse your towels do any good?

Linen reuse programs, where you forgo daily replacement of towels and hopefully sheets, have become the norm – and actually save huge amounts of water, as well as detergent and energy. If you’re traveling, you really should take part in this; many people still don’t do this.

However, when it comes to assessing a hotel’s environmental performance, a towel program should be standard practice.

PSA for any hotel operators: According to social psychology research, more people will participate if you exploit a “general norms” approach when presenting it. Posters should say “Join other guests in saving water” – rather than presenting it in a more altruistic way, such as “Help protect the environment by reusing towels.”

Look for substantiated claims

The best way to evaluate a hotel is to seek credible third-party certifications from scientific standards-setting and mandatory auditing programs such as the Global Sustainable Tourism Council (GSTC) and EarthCheck. The more familiar LEED certification, particularly the platinum standard, is best-in-class for a hotel’s design, but says little about its day-to-day operations or its local environmental and economic impact. In nature-rich regions, the nonprofit Rainforest Alliance also certifies hotels that meet certain sustainability and biodiversity conservation standards.

What to avoid: Self-created credentials or environmental awards. These signs on websites and at hotel check-in counters – “The best eco-friendly hotel!” or “Voted most sustainable hotel in town!” – they are often a marketing trick or the result of paid promotion.

Many companies claim zero waste but often rely on single-use products that claim to be compostable or biodegradable but are not actually composted; They also exploit energy and novel natural resources for production, even if they are later composted. Others make pledges to reduce plastic, which are often narrow in scope and apply to individual items such as cups or cutlery, but ignore others; or switching to boxed water instead of bottled water, even though the boxes are made of plastic and are only slightly recyclable.

Unfortunately, there is no straightforward tool to fact-check such claims because there are actually no laws governing what companies can say about how environmentally amiable they are.

In recent days fantastic question caught the attention of Novel Yorkers and local tabloids: Who goes in and out of the drains throughout the city and what are they doing in the sewer system?

ON May 5Surveillance footage shows three people wearing waders entering a manhole in Queens. Then in the early morning hours at May 29another camera captured a group of people emerging from a sewer manhole in Brooklyn. On the same day a miscellaneous the group was seen emerging from another manhole in Brooklyn, miles away from the first site. Some wore it headlightsand some carried what seemed to be shovels AND flashlights.

The Novel York Police Department has already done this was speculated that men are scavengers looking for jewelry, weapons or other valuables. But no one knows for sure, so WIRED consulted several urban exploration content creators based in Novel York. On platforms such as TikTok, YouTube and Instagram, ‘urbex’ creators – typically teenage boys or juvenile men who film together in tiny groups – explore abandoned or hard-to-reach spaces such as disused factories, dilapidated mansions and underground tunnels.

The creators who spoke to WIRED say they did not recognize anyone in the video. In fact, they did not consider the alleged sewage bandits as their own.

There’s nothing of value there except “doo doo water and a few needles,” says one of the creators. “And sewers are quite risky because there is basically no cell phone reception there.” (Because this type of mining is illegal, the creators spoke on condition of anonymity.)

“No one does sewerage,” says another creator when WIRED asked if manhole workers could be part of the urbex community. “It’s just a very old system and people don’t know much about it.” The men in the films “were too sophisticated about it,” says the creator, pointing out that some change clothes after they appear.

Another creator said subway tunnels and abandoned stations in the city were better places to film, noting that it could capture close-up shots of trains and “prestige graffiti.”

In 2010, the Novel York Times published an article entitled guided tour underground Novel York City, including portions of the sewer system that have entrances to Van Cortlandt Park in the Bronx and Kissena Park in Queens. The article included descriptions of “condoms and sticky bits of toilet paper” floating in the “coffee-colored gloom,” but no miraculous graffiti.

Another creator claims that the potential discovery could be the entrance to a sewer manhole abandoned trolleybus tracks. They claim to know “a few people who have entered manholes just for the thrill of exploration,” but add that this was “a long time ago” and that “no one in today’s urbex scene really knows which manholes need to be opened to access trolley lines.”

“A lot of people going to different channels throughout New York seem suspicious to me,” the creator says. “It could be more than just exploration.”

The NYPD and the Novel York City Department of Environmental Protection, which oversees the sewer system, tell WIRED that they have investigated the sewer locations shown in the surveillance video and determined that the situation does not pose a threat to public safety. (The DEP also emphasized that such activity is “both illegal and extremely dangerous.”)

WIRED was unable to locate any recent urbex content featuring Novel York City’s sewer systems. The most popular content types are subway tunnels, abandoned subway stations, and non-public rooftops of Manhattan skyscrapers.

OpenAI and Anthropic they fought for employees, customers and public attention. Rival AI labs have argued on opposing sides of policy proposals, and their CEOs were the only ones who didn’t shake hands with a dozen industry leaders at a business summit earlier this year. But they do have one massive area of ​​overlap: their investors.

Over the past few years, about 90 venture capital firms and other money managers have invested in both OpenAI and Anthropic, according to a WIRED analysis of data from PitchBook, a platform that tracks startup investments. Data shows that OpenAI shares approximately 42 percent of its total investors with Anthropic. Roughly a third of Anthropic’s investors are also OpenAI supporters, including immense companies such as Sequoia Capital, Greylock, Founders Fund, Redpoint Ventures, Emerson Collective and Sound Ventures.

Last week, Anthropic created collection announcement which listed 31 investors, at least 13 of whom have shares in OpenAI, according to PitchBook data and WIRED reporting. The number of common investors may be underestimated because collecting information on private investments is challenging. WIRED identified at least several investors in PitchBook data who are not on OpenAI’s roster, including: Amazon.

The degree of overlap is astonishing for two fierce competitors who started fundraising several years apart. Three experts who study the venture capital industry called this prevalence unusual, even unprecedented. This phenomenon reflects the recent evolution of the venture capital industry, the emergence of two extraordinary companies that have raised unprecedented sums of money, and the wide-open competition between them and others in the field of artificial intelligence.

“The ownership structure you see today gives a real insight into how sophisticated investors view this market, and the answer seems to be that few are convinced that this will be a winner-takes-all market, and if so, who will be the dominant players,” says Tom Nicholas, Harvard Business School professor and author of the book VC: American History.

The investor crossover is also noteworthy, as Anthropic and OpenAI aim to debut on the stock exchange this year. Initial public offerings are often an opportunity for investors to realize profits from owning a startup. But last yearonly two-thirds of IPOs saw a significant raise in value. By betting on both OpenAI and Anthropic, investors can double their chances of success.

“Rather than looking at these companies as overlapping technologies, these large investors are protecting their ability to generate profits,” says Kyle Stanford, director of venture capital research at PitchBook.

OpenAI and Anthropic did not respond to requests for comment. Several venture capital firms that have invested in OpenAI and Anthropic also declined or did not respond to requests for comment on why they decided to back both.

Several spoke only on the condition of anonymity to avoid jeopardizing industry relationships, and each called the matchup an investment opportunity with OpenAI and Anthropic unlike any they had encountered before.

Historically, venture capital firms have concentrated their bets on one company in an area of ​​competition that should be avoided conflicts of interestsays Stanford. Companies sometimes share proprietary information with investors or look to them for advice or management, and owning shares in rivals encourages awkward conversations.

On Wednesday evening, the U.S. Department of Agriculture announced that a case of the Novel World snail had been confirmed in south Texas. This is the first violation of the US-Mexico border detected by the US voracious, carnivorous fliesthat have they climbed up through Central America for several years.

IN social media post on Wednesday afternoonThe USDA disclosed that the Texas sample was sent to the National Veterinary Services Laboratories (NVSL) in Ames, Iowa, for confirmation of worm infection. Secretary of Agriculture Brooke Rollins later posted it the examination confirmed the infectionthat was found in a three-week-old calf in Zavala County, Texas.

News of the worm’s detection was already building this week, sending shockwaves through the U.S. cattle industry.

Although many animals, including humans, can be victims of the parasite, the snail is particularly threatening to farm animals. Female snails lay hundreds of eggs in the wounds and holes of warm-blooded creatures, allowing their larvae to feed on living animals, causing deep, festering, life-threatening wounds. Although the snail was once endemic to the U.S., it was extirpated in the 1960s as a result of years of control efforts. The USDA estimates that keeping snails out of the United States will saved the livestock industry $900 million a year.

But the fly had broken through the controls in Central America and was getting closer. According to the USDA, on May 28, a case of the infection was detected within 25 miles of the border in a five-year-old goat in Coahuila, Mexico. The case was one of many detected in recent days, including one in a calf just 60 km from the border, also in Coahuila.

Disputed findings

In a media call Tuesday, Agriculture Secretary Brooke Rollins said, “There’s no question that this is a very, very serious threat to our livestock.” But she also disputed claims that the fly is closer or even already in the US.

On Monday, state Rep. Don McLaughlin claimed on social media that the snailworm case was rightly found one mile from the Texas borderwhich Rollins and The USDA denied it.

“When false information comes to light, there is huge panic.” Rollins said Tuesday, according to the Texas Tribune. “And rightfully so, especially when it comes from elected officials and the media.”

on Wednesday, Reuters reported that McLaughlin suspected the fly was already here. He said samples taken Tuesday from two calves at a ranch in La Pryor, Texas – in Zavala County, where the worm infection was confirmed – were being tested for possible snail infection. One infection is said to have occurred in the umbilical cord wound of one of the calves. McLaughlin said he had seen photos and videos of the animals and that the larvae in the photos looked like snail larvae.

Reuters was shown one photo that it said showed “multiple snail-like larvae inside a bloody circular wound on the animal,” but said it “could not immediately verify the photo.”

“At this point, it is unconfirmed that it is a New World snail,” McLaughlin said Wednesday. “It looks like it, but it’s unconfirmed.”

The USDA said the discovery has now been confirmed press release on Wednesday evening that it is forming a “unified incident command team” with the Texas Animal Health Commission and sending response personnel to the area. It also establishes a 20-kilometer zone around detected infection for quarantine, movement restrictions and increased surveillance and fly trapping.

Back Screw

The snails were wiped out in the US in the 1960s as a result of concerted efforts to wipe out their populations. This is done by aerial bombardment of sterile male flies, which is the most effective weapon against parasites. The mass release of duds displaces fertile males, preventing them from mating with females, which typically mate only once.

Thanks to this method, called the sterile insect technique, flies were eradicated not only in the US but throughout Central America. In 2006, they were declared exterminated in Panama.