The Ultimate Scikit-Learn Machine Learning Cheatsheet

The Ultimate Scikit-Learn Machine Learning Cheatsheet. With the power and popularity of the scikit-learn for machine learning in Python. Preview its core methods with this review of predictive modelling, clustering, dimensionality reduction, feature importance, and data transformation.

With the power and popularity of the scikit-learn for machine learning in Python, this library is a foundation to any practitioner's toolset. Preview its core methods with this review of predictive modelling, clustering, dimensionality reduction, feature importance, and data transformation.

There are several areas of data mining and machine learning that will be covered in this cheat-sheet:

• Predictive Modelling Regression and classification algorithms for supervised learning (prediction), metrics for evaluating model performance.
• Methods to group data without a label into clusters: K-Means, selecting cluster numbers based objective metrics.
• Dimensionality Reduction Methods to reduce the dimensionality of data and attributes of those methods: PCA and LDA.
• Feature Importance Methods to find the most important feature in a dataset: permutation importance, SHAP values, Partial Dependence Plots.
• Data Transformation Methods to transform the data for greater predictive power, for easier analysis, or to uncover hidden relationships and patterns: standardization, normalization, box-cox transformations.

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