ML Best Practices

Clustering:

• Understand the schema and field descriptions.
• Visualize features referenced in the prompt (e.g., with histograms, scatterplots).
• Transform dates into timestamps.
• Before applying encoders, check if the dataset already contains pre-encoded features and prefer existing numerical representations.
• Prefer to keep data instead of dropping it when possible.
• Transform ordinal data with an ordinal encoder.
• Transform nominal data with a one hot encoder.
• Standardize numerical features.
• Perform clustering with a range of values, and collect the silhouette score.
• Choose the optimal number of clusters based on the silhouette score.
• Use dimensionality reduction (e.g., PCA) to project the data into two dimensions.
• Scatterplot the samples in two dimensions with cluster labels as the hue.
• Scatterplot the samples in two dimensions with a discrete feature as the hue.
• Describe the clusters in text by feature distributions or typical feature values.
• Conclusion: comprehensively answer the prompt in a final markdown cell.

Time Series Forecasting:

• Understand the schema and field descriptions.
• Visualize the target feature over time at a reasonable granularity.
• Always perform a chronological split on the data to create training, validation, and test sets.
• Are there seasonal trends?
• Test for stationarity.
• Discuss possible modeling approaches. How might different modeling approaches impact the prediction accuracy?
• Train two time series forecasting models to predict the target feature. Use previous seasonality and stationarity information as model hyperparameters.
• Predict the target feature for the training and validation sets.
• Optionally, hypertune models with the validation set.
• Visualize the actual and predicted target feature vs time for each model on the training and validation sets.
• Evaluate the validation performance with error metrics.
• Select a model.
• Retrain the selected model on the test and validation sets.
• Predict the test values with the selected model.
• Visualize the average target feature and the predicted test values.
• Conclusion: comprehensively answer the prompt in a final markdown cell.

Exploratory Data Analysis / Anomaly Detection:

• Understand the schema and field descriptions.
• Visualize the target feature distribution in a way that shows outliers.
• Identify and describe any outliers in the target feature.
• Visualize relationships between the target feature and other features.
• Identify and describe unusual patterns or significant trends.
• Visualize patterns and trends.
• Conclusion: comprehensively answer the prompt in a final markdown cell.

Classification:

• Understand the schema and field descriptions.
• Identify rows that don't make sense. How many are there and what do they contain?
• Identify rows without a target value. How many are there and what do they contain?
• Drop rows that don't match the schema or don't have the target value (if it is reasonable to do so).
• Split data into training, validation, and test sets.
• Create features to represent when data are missing, if this is meaningful.
• Handle missing data. Prefer to keep data instead of dropping it when possible.
• Before applying encoders, check if the dataset already contains pre-encoded features and prefer existing numerical representations.
• Transform ordinal data with an ordinal encoder.
• Transform nominal data with a one hot encoder.
• Standardize numerical features.
• Train multiple models.
• If there is evidence of overfitting, regularize and retrain the model.
• If there is evidence of underfitting, consider adding or engineering features.
• Evaluate the models.
• Create confusion matrices.
• Conclusion: comprehensively answer the prompt in a final markdown cell.

Regression:

• Understand the schema and field descriptions.
• Identify rows that don't make sense. How many are there and what do they contain?
• Identify rows without a target value. How many are there and what do they contain?
• Develop an understanding of the data and determine how to handle missing values. This should make sense in the business context.
• Identify any potential sources of group leakage. Aggregate where appropriate to prevent this.
• Visualize target feature.
• Split data into training, validation, and test sets.
• Handle missing data. Prefer to keep data instead of dropping it when possible.
• Before applying encoders, check if the dataset already contains pre-encoded features and prefer existing numerical representations.
• Transform ordinal data with an ordinal encoder.
• Transform nominal data with a one hot encoder. Restrict high cardinality categorical features to a tractable size.
• Standardize numerical features.
• Train multiple models.
• Visualize the actual vs predicted values on training and validation data.
• If there is evidence of overfitting, regularize and retrain the model.
• If there is evidence of underfitting, consider adding or engineering features.
• Evaluate the model error.
• Conclusion: comprehensively answer the prompt in a final markdown cell.

Comparing ML Models:

• Understand the schema and align metrics with business goals (e.g., cost of false positives vs. false negatives).
• Establish baselines: define a naive baseline (majority class/mean) and a simple ML baseline (e.g., Logistic/Linear Regression).
• Ensure rigorous validation: use identical, fixed data splits for all models and perform $k$-fold cross-validation.
• If data is temporal, use chronological splits for validation.
• Select and report metrics beyond accuracy (e.g., F1-Score, PR-AUC, MAE, RMSE) that reflect business impact.
• Use bootstrapping to calculate 95% confidence intervals for key metrics to determine statistical significance.
• Perform slice-based error analysis: evaluate model performance across key subpopulations and demographics to identify bias or specific failure modes.
• Inspect and compare confusion matrices, residual plots, and calibration curves.
• Evaluate operational trade-offs: consider inference latency, training time, compute cost, and model size.
• Assess interpretability using tools like SHAP or LIME where transparency is required.
• Conclusion: Recommend the optimal model for the specific use case, justifying the choice with both performance and production viability.

No match:

• Understand the schema and field descriptions.
• Identify rows that don't make sense. How many are there and what do they contain?
• Identify rows without a target value. How many are there and what do they contain?
• Drop rows that don't match the schema or don't have the target value (if it is reasonable to do so).
• Create features to represent when data are missing, if this is meaningful.
• Handle missing data. Prefer to keep data instead of dropping it when possible.
• Before applying encoders, check if the dataset already contains pre-encoded features and prefer existing numerical representations.
• Transform ordinal data with an ordinal encoder.
• Transform nominal data with a one hot encoder.
• Standardize numerical features.
• Conclusion: comprehensively answer the prompt in a final markdown cell.

Essential ML Practices

• Strict Featurization Ordering: For supervised learning ALWAYS split the dataset into training and test data BEFORE fitting preprocessing pipelines (e.g. scaling, encoding). Fit the pipelines on the training data and test data independently.
• Handling Missing or NULL Values: ALWAYS check for and handle missing and NULL values. First, analyze their frequency. Then, decide whether to keep them, drop them or impute them with a contextually appropriate value, and explain your reasoning.