What are the best ways to partition data for training and testing?

I devided historical data into training and testing sets in finance to estimate stock values. This maintained the real-world robustness of our model and avoided overdrifting.

Certainly, here are concise points to consider for your perspective on partitioning data for training and testing: Randomized Splitting: Emphasize the importance of random data splitting to prevent bias and overfitting. Stratified Sampling: Highlight its role in maintaining proportional representation, especially in imbalanced datasets. Time-Based Partitioning: Discuss its significance in preserving the temporal order for time-series data. Cross-Validation Techniques: Advocate for using methods like k-fold cross-validation for robust model evaluation. Validation Set Usage: Emphasize the need for a separate validation set to fine-tune models and prevent overfitting during training.

Splitting data is like giving your machine learning model an exam. We divide it into "training" and "testing" sets—the former educates the model, the latter evaluates it. To avoid bias, we shuffle data and use "stratified sampling." "Cross-validation" tests subsets, and for time series, older data trains while newer data tests. A "validation" set fine-tunes the model.

Proper data partitioning isn't just about how well the model works; it's also about how trustworthy it is. Stakeholders and clients who rely on the insights generated by these models require assurance that the model will perform consistently in real-world scenarios. Models that have only been tried and tested on training data can produce misleading results, potentially leading to poor business decisions or misguided strategies. Not dealing with overfitting can also hurt the analysis's credibility and the reputation of the team or individual analyst who did it.

I rely pretty much on the old 70-30 test train split which is a really good way to get a first iteration before fine tuning the model further.

There are 5 key partition technique used in the industry. Train-Test split - a 70:30 split for training vs Test Train-Validation-Test Split: based on each epoch, data is divided into these 3 brackets K-Fold Cross Validation - dataset is divided into k subsets or folds where model is usually trained and evaluated k times, using a separate fold as the validation set each time. Stratified Sampling for data imbalanced scenarios. Time-based Split where time-series is the key

I believe that picking between holdout and k-fold cross-validation isn't just a matter of procedure; it also depends on the data and the business problem that needs to be solved. For example, in a time-series dataset where data order is important, techniques such as time-series split may be more appropriate than random k-fold cross-validation. In projects with limited data, bootstrapping may be a way to artificially augment the data, though this comes with its own set of challenges.

- The most common method is random splitting, where you randomly shuffle the data and allocate a percentage (70% for training and 30% for testing) to each set. Libraries like scikit-learn (Python) offer functions for this. - When dealing with imbalanced datasets (e.g., rare disease cases), stratified sampling ensures that both sets maintain the same class distribution as the original dataset. - For time-series data, you often partition data sequentially, with the training set covering earlier periods and the testing set covering later periods. - In some cases, k-fold cross-validation is used, where the dataset is split into k subsets, and the model is trained and tested k times, rotating which subset serves as the test set each time.

Instead of the conventional 70-30 or 80-20 split, one can opt for k-fold cross-validation, a method that involves dividing the dataset into k subsets, training the model k times, each time using a different subset as the test set and the remaining subsets as the training set. This approach provides a more robust assessment of model performance by ensuring that each data point is used for testing at least once, enabling a comprehensive understanding of the model's generalization capability.

It is recommended to use 70% or 80% of the data for training and the rest for the model validation, but with a large amout of data, we can go as far as using 90% of data for training and still have enough data to test the model. We don't have to stick to a single technique for splitting data either, we can try other techniques such as cross validation to train and test our model.

From what I've seen, tools and libraries make data partitioning easier, but domain knowledge is still very important for how it's done. It's critical to understand the nuances and quirks of the dataset you're working with. Certain samples may be of particular importance due to seasonal fluctuations or rare but significant events. In this case, a simple random split might leave these samples out of the training or testing set, which could make it harder for the model to understand what happened.

Utilize programming libraries like scikit-learn (Python), caret (R), or other tools specific to your language of choice. They offer functions and methods for partitioning data efficiently.

Choosing a suitable data storage system or database is very important that should support partitioning, such as relational databases (e.g., MySQL), NoSQL or cloud-based solutions (e.g., Google BigQuery).

To implement data partitioning, split your dataset into two parts: one for training, one for testing. Shuffle data randomly, use time order for chronology, or leave out pieces for small datasets. Maintain a fair mix in both sets, especially with diverse groups (stratified sampling). Experiment with methods like k-fold cross-validation for optimal results. Balance is crucial for effective learning and performance on new data.

Para particionar dados de forma simples, agrupe-os conforme uma característica comum, como datas ou categorias. Isso facilita a organiza??o e acesso aos dados relevantes quando necessário.

-Ensure randomness when splitting data to avoid any bias. Set a random seed for reproducibility. -Use stratified sampling to maintain the class distribution when dealing with imbalanced datasets. -Depending on the dataset size, a common practice is to allocate 70-80% for training and the rest for testing. However, this can vary based on the problem. -Consider cross-validation (e.g., k-fold) to get a more robust estimate of model performance, especially when the dataset is small. -Ensure that data preprocessing (e.g., normalization, scaling) is consistent across training and testing sets. -Be cautious to exclude any information from the testing set during the model training phase, which can lead to overfitting.

Data partitioning is vital for training and testing machine learning models. To do this effectively: Optimal Ratio: Allocate 2/3 of data for training and 1/3 for testing to strike a balance between training and evaluation. Stratified Sampling: Use this when dealing with imbalanced datasets to maintain class distribution for unbiased model evaluation. Validation Set: Create a validation set for hyperparameter tuning and model selection. Various techniques are available: sklearn's train_test_split(), numpy's randn() for synthetic datasets, and Pandas' sample() for random selection. By following these practices and techniques, you can achieve accurate model evaluation for informed decisions in model development.

Similar to slicing a roast, data partitioning requires technique. There is no one slice that fits all models, so you must experiment to see which cut works best. To ensure reproducible partitioning magic, use a random seed because consistency is crucial. Don't forget the skewed parts; use stratified sampling to avoid flavor imbalances. Don't depend just on one statistic when the model is ready to be tested. Assess it using a wide range of metrics, including recall, accuracy, precision, F1-score, and the ROC curve! Furthermore, keep in mind that in order to prevent the flavor from being compromised, division and seasoning should be done separately. Cheers, data analyst!

1. Loading and Exploring the Data 2. Method and Criteria for Data Partitioning 3. Partitioning the Data 4. Using the Data Subsets 5. Model Evaluation 6. Iterative Process Actual projects may vary in complexity, data size, and specific techniques used. Always tailor your approach to the unique requirements of the project at hand.

Remember that the best practices for data partitioning can vary depending on the specific database system you're using, so it's essential to consult the system's documentation and consider system-specific guidelines.

The best way for learning Data Partitioning is look around yourself and nature you will get idea how things similar to reach other are categorize, then similar things are further categorize base on some traits the process goes on. After that start analysis data from simple to complex data you can find on kaggle segregate them and do data partitioning techniques. You will be expert in 3 to 4 Months if you give 2 to 3 hours days daily on it

- online machine learning and data analysis courses covering data partitioning techniques. - Machine learning books, such as "Introduction to Machine Learning with Python" by Andreas C. Müller and Sarah Guido, often have data splitting and validation sections. - Libraries like Scikit-learn and R's Caret have extensive documentation and examples on data partitioning. -Platforms like Medium, Towards Data Science, and Stack Overflow often have informative articles and discussions on best practices for data partitioning. -Collaborating with experienced data scientists or mentors can provide valuable insights and hands-on experience in best practices in data partitioning and machine learning.

In addition to the outlined resources, the best way to learn is to practice partitioning on datasets, test the resulting impact on model performance i.e. how well is a model trained and how well does it predict on new data, and therefore learn from trial and error.

Remember that the specific resources you choose should align with your current level of knowledge and the database system you're working with or interested in. Learning about data partitioning is a continuous process, and staying up-to-date with industry best practices and new developments is essential.

In the data mining context, I may suggest Data Mining: Concepts and Techniques by Jiawei Han , Micheline Kamber ... In a raw data context, read the fine manual of your db. In a data mesh context, I suggest Data Mesh: Delivering Data-Driven Value at Scaleby Zhamak Dehghani.

Key points on partitioning data for training and testing: - Proper data partitioning is essential for robust AI models. - Balancing training and testing sets avoids bias in model evaluation. - Randomization within partitions ensures unbiased performance assessment. - Cross-validation offers a more comprehensive evaluation. - Stratified sampling can be critical for imbalanced datasets. - Time-based data splitting reflects real-world scenarios. - The art of data partitioning shapes AI success.

How To Address Biased Data Partitioning in Real-Life Scenarios Real-life biases result from imbalanced class distributions and skewed sampling. To combat bias: Sufficient Data: Use diverse datasets to mitigate limitations and skewness. Data Augmentation: Employ oversampling for minority classes or undersampling for majorities in imbalanced datasets. Feature Engineering: Carefully select and refine features to reduce biases, utilizing domain knowledge. Implementing these strategies in data partitioning ensures fair, unbiased model development, yielding accurate predictions and insights. Randomized Partitioning: Randomly allocate data to avoid systematic bias. Cross-Validation: Use multiple subsets for unbiased model evaluation.

data partitioning helps organize and manage data more effectively, making it easier to derive meaningful insights for decision-making, optimization, and personalization. The key to gaining valuable insights lies in selecting the right partition key and understanding the query patterns associated with your data.

Whatever your task is, always keep in mind the domain needs. Before applying any techinique, learn about the field in which you are applying it. A technique in medicine that make sense in finance can be a complete nonsense.

Impact of Data Characteristics on Partitioning Decisions: Delve into how the intrinsic characteristics of a dataset, such as its size, feature distribution, and type of variables (categorical vs. continuous), influence the choice of partitioning strategy. For instance, extremely large datasets might necessitate different sampling approaches compared to smaller ones. Similarly, datasets with a high number of categorical variables might require careful consideration to ensure that all categories are well-represented in both the training and testing sets. This point will guide practitioners to tailor their partitioning approach based on the specific characteristics of their data, leading to more effective model training and validation.