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scikit-learn
scikit - learn # scikit -learnTable of ContentsAbout1 Chapter 1: Getting started with scikit -learn2 Remarks2 Examples2 Installation of scikit -learn2 Train a classifier with cross-validation2 Creating pipelines3 Interfaces and conventions:4 Sample datasets4 Chapter 2: Classification6 Examples6 Using Support Vector Machines6 RandomForestClassifier6 Analyzing Classification Reports7 GradientBoostingClassifier8A Decision Tree8 Classification using Logistic Regression9 Chapter 3: Dimensionality reduction (Feature selection)11 Examples11 Reducing The Dimension With Principal Component Analysis11 Chapter 4: Feature selection13 Examples13 Low-Variance Feature Removal13 Chapter 5: Model selection15 Examples15 Cross-validation15K-Fold Cross Validation15K-Fold16 ShuffleSplit16 Chapter 6: Receiver Operating Characteristic (ROC)17 Examples17 Introduction to ROC and AUC17 ROC-AUC score with overriding and cross validation18 Chapter 7: Regression20 Examples20 Ordinary Least Squares20 Credits22 AboutYou can share this PDF with anyone you feel could benefit from it, downloaded the latest version from: scikit -learnIt is an unofficial and free scikit - learn ebook created for educational purposes.
Chapter 1: Getting started with scikit-learn Remarks scikit-learn is a general-purpose open-source library for data analysis written in python. It is based on other python libraries: NumPy, SciPy, and matplotlib scikit-learncontains a number of implementation for different popular algorithms of machine learning.
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