Discover how AutoGluon, an open-source machine learning library developed by Amazon Web Services, automates the entire ML pipeline, including data preprocessing, feature engineering, model training, and evaluation. With just four lines of code, learn how AutoGluon delivers top-notch performance by employing techniques like ensemble learning and automatic hyperparameter tuning.
PySpark for time-series data, discussing data ingestion, extraction, and visualization with practical implementation code.
This article provides a comprehensive guide to performing exploratory data analysis on time series data, with a focus on feature engineering.
Cool question - and yes, you're right that you can use the summary command to inspect feature_importances for some of the models (e.g. RandomForestClassifier). Other models may not support the same type of summary however.
You should also check out the FieldSelector algorithm which is really useful for this problem. Under the hood, it uses ANOVA & F-Tests to estimate the linear dependency between variables. Although its univariate (not capturing any interactions between variables), it still can provide a good baseline from choosing a handful of features from hundreds.
You should also check out the FieldSelector algorithm which is really useful for this problem. Under the hood, it uses ANOVA & F-Tests to estimate the linear dependency between variables. Although its univariate (not capturing any interactions between variables), it still can provide a good baseline from choosing a handful of features from hundreds.
