Scikit-Learn Cookbook · 2nd Edition by Avila, Julian -- Read -- Imperial Library of Trantor

Index

Title Page

scikit-learn Cookbook

Second Edition

scikit-learn Cookbook

Second Edition

Credits About the Authors About the Reviewer www.PacktPub.com

Why subscribe?

Customer Feedback Preface

What this book covers Who this book is for What you need for this book Conventions Reader feedback Customer support

Downloading the example code Errata Piracy Questions

High-Performance Machine Learning – NumPy

Introduction NumPy basics

How to do it...

The shape and dimension of NumPy arrays NumPy broadcasting Initializing NumPy arrays and dtypes Indexing Boolean arrays Arithmetic operations NaN values

How it works...

Loading the iris dataset

Getting ready How to do it... How it works...

Viewing the iris dataset

How to do it... How it works... There's more...

Viewing the iris dataset with Pandas

How to do it... How it works...

Plotting with NumPy and matplotlib

Getting ready How to do it...

A minimal machine learning recipe – SVM classification

Getting ready How to do it... How it works... There's more...

Introducing cross-validation

Getting ready How to do it... How it works... There's more...

Putting it all together

How to do it... There's more...

Machine learning overview – classification versus regression

The purpose of scikit-learn

Supervised versus unsupervised

Getting ready How to do it...

Quick SVC – a classifier and regressor Making a scorer

How it works... There's more...

Linear versus nonlinear Black box versus not

Interpretability

A pipeline

Pre-Model Workflow and Pre-Processing

Introduction Creating sample data for toy analysis

Getting ready How to do it...

Creating a regression dataset Creating an unbalanced classification dataset Creating a dataset for clustering

How it works...

Scaling data to the standard normal distribution

Getting ready How to do it... How it works...

Creating binary features through thresholding

Getting ready How to do it... There's more...

Sparse matrices The fit method

Working with categorical variables

Getting ready How to do it... How it works... There's more...

DictVectorizer class

Imputing missing values through various strategies

Getting ready How to do it... How it works... There's more...

A linear model in the presence of outliers

Getting ready How to do it... How it works...

Putting it all together with pipelines

Getting ready How to do it... How it works... There's more...

Using Gaussian processes for regression

Getting ready How to do it…

Cross-validation with the noise parameter

There's more...

Using SGD for regression

Getting ready How to do it… How it works…

Dimensionality Reduction

Introduction Reducing dimensionality with PCA

Getting ready How to do it... How it works... There's more...

Using factor analysis for decomposition

Getting ready How to do it... How it works...

Using kernel PCA for nonlinear dimensionality reduction

Getting ready How to do it... How it works...

Using truncated SVD to reduce dimensionality

Getting ready How to do it... How it works... There's more...

Sign flipping Sparse matrices

Using decomposition to classify with DictionaryLearning

Getting ready How to do it... How it works...

Doing dimensionality reduction with manifolds – t-SNE

Getting ready How to do it... How it works...

Testing methods to reduce dimensionality with pipelines

Getting ready How to do it... How it works...

Linear Models with scikit-learn

Introduction Fitting a line through data

Getting ready How to do it... How it works... There's more...

Fitting a line through data with machine learning

Getting ready How to do it...

Evaluating the linear regression model

Getting ready How to do it... How it works... There's more...

Using ridge regression to overcome linear regression's shortfalls

Getting ready How to do it...

Optimizing the ridge regression parameter

Getting ready How to do it... How it works... There's more...

Bayesian ridge regression

Using sparsity to regularize models

Getting ready How to do it... How it works...

LASSO cross-validation – LASSOCV

LASSO for feature selection

Taking a more fundamental approach to regularization with LARS

Getting ready How to do it... How it works... There's more...

References

Linear Models – Logistic Regression

Introduction

Using linear methods for classification – logistic regression

Loading data from the UCI repository

How to do it...

Viewing the Pima Indians diabetes dataset with pandas

How to do it...

Looking at the UCI Pima Indians dataset web page

How to do it...

View the citation policy Read about missing values and context

Machine learning with logistic regression

Getting ready

Define X, y – the feature and target arrays

How to do it...

Provide training and testing sets Train the logistic regression Score the logistic regression

Examining logistic regression errors with a confusion matrix

Getting ready How to do it...

Reading the confusion matrix General confusion matrix in context

Varying the classification threshold in logistic regression

Getting ready How to do it...

Receiver operating characteristic – ROC analysis

Getting ready

Sensitivity A visual perspective

How to do it...

Calculating TPR in scikit-learn Plotting sensitivity

There's more...

The confusion matrix in a non-medical context

Plotting an ROC curve without context

How to do it...

Perfect classifier Imperfect classifier AUC – the area under the ROC curve

Putting it all together – UCI breast cancer dataset

How to do it...

Outline for future projects

Building Models with Distance Metrics

Introduction Using k-means to cluster data

Getting ready How to do it… How it works...

Optimizing the number of centroids

Getting ready How to do it... How it works...

Assessing cluster correctness

Getting ready How to do it... There's more...

Using MiniBatch k-means to handle more data

Getting ready How to do it... How it works...

Quantizing an image with k-means clustering

Getting ready How do it… How it works…

Finding the closest object in the feature space

Getting ready How to do it... How it works... There's more...

Probabilistic clustering with Gaussian mixture models

Getting ready How to do it... How it works...

Using k-means for outlier detection

Getting ready How to do it... How it works...

Using KNN for regression

Getting ready How to do it… How it works..

Cross-Validation and Post-Model Workflow

Introduction Selecting a model with cross-validation

Getting ready How to do it... How it works...

K-fold cross validation

Getting ready How to do it.. There's more...

Balanced cross-validation

Getting ready How to do it... There's more...

Cross-validation with ShuffleSplit

Getting ready How to do it...

Time series cross-validation

Getting ready How to do it... There's more...

Grid search with scikit-learn

Getting ready How to do it... How it works...

Randomized search with scikit-learn

Getting ready How to do it...

Classification metrics

Getting ready How to do it... There's more...

Regression metrics

Getting ready How to do it...

Clustering metrics

Getting ready How to do it...

Using dummy estimators to compare results

Getting ready How to do it... How it works...

Feature selection

Getting ready How to do it... How it works...

Feature selection on L1 norms

Getting ready How to do it... There's more...

Persisting models with joblib or pickle

Getting ready How to do it...

Opening the saved model

There's more...

Support Vector Machines

Introduction Classifying data with a linear SVM

Getting ready

Load the data Visualize the two classes

How to do it... How it works... There's more...

Optimizing an SVM

Getting ready How to do it...

Construct a pipeline Construct a parameter grid for a pipeline Provide a cross-validation scheme Perform a grid search

There's more...

Randomized grid search alternative Visualize the nonlinear RBF decision boundary More meaning behind C and gamma

Multiclass classification with SVM

Getting ready How to do it...

OneVsRestClassifier Visualize it

How it works...

Support vector regression

Getting ready How to do it...

Tree Algorithms and Ensembles

Introduction Doing basic classifications with decision trees

Getting ready How to do it...

Visualizing a decision tree with pydot

How to do it... How it works... There's more...

Tuning a decision tree

Getting ready How to do it... There's more...

Using decision trees for regression

Getting ready How to do it... There's more...

Reducing overfitting with cross-validation

How to do it... There's more...

Implementing random forest regression

Getting ready How to do it...

Bagging regression with nearest neighbors

Getting ready How to do it...

Tuning gradient boosting trees

Getting ready How to do it... There's more...

Finding the best parameters of a gradient boosting classifier

Tuning an AdaBoost regressor

How to do it... There's more...

Writing a stacking aggregator with scikit-learn

How to do it...

Text and Multiclass Classification with scikit-learn

Using LDA for classification

Getting ready How to do it... How it works...

Working with QDA – a nonlinear LDA

Getting ready How to do it... How it works...

Using SGD for classification

Getting ready How to do it... There's more...

Classifying documents with Naive Bayes

Getting ready How to do it... How it works... There's more...

Label propagation with semi-supervised learning

Getting ready How to do it... How it works...

Neural Networks

Introduction Perceptron classifier

Getting ready How to do it... How it works... There's more...

Neural network – multilayer perceptron

Getting ready How to do it... How it works...

Philosophical thoughts on neural networks

Stacking with a neural network

Getting ready How to do it...

First base model – neural network Second base model – gradient boost ensemble Third base model – bagging regressor of gradient boost ensembles Some functions of the stacker Meta-learner – extra trees regressor

There's more...

Create a Simple Estimator

Introduction Create a simple estimator

Getting ready How to do it... How it works... There's more...

Trying the new GEE classifier on the Pima diabetes dataset Saving your trained estimator

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