scikit-learn
scikit - learn # scikit -learnTable of ContentsAbout1Chapter 1: Getting started with scikit -learn2Remarks2Examples2Installati on of scikit -learn2Train a classifier with cross-validation2Creating pipelines3Interfaces and conventions:4Sample datasets4Chapter 2: Classification6Examples6Using Support Vector Machines6RandomForestClassifier6Analyzin g Classification Reports7GradientBoostingClassifier8A Decision Tree8Classification using Logistic Regression9Chapter 3: Dimensionality reduction (Feature selection)11Examples11Reducing The Dimension With Principal Component Analysis11Chapter 4: Feature selection13Examples13Low-Variance Feature Removal13Chapter 5: Model selection15Examples15Cross-validation15K -Fold Cross Validation15K-Fold16ShuffleSplit16Chapte r 6: Receiver Operating Characteristic (ROC)17Examples17Introduction to ROC and AUC17ROC-AUC score with overriding and cross validation18Chapter 7: Regression20Examples20Ordinary Least Squares20Credits22AboutYou 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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