scikit-learn Cookbook - Second Edition

所需积分/C币:9 2018-04-21 15:07:18 6.91MB PDF

Learn to use scikit-learn operations and functions for Machine Learning and deep learning applications. About This Book Handle a variety of machine learning tasks effortlessly by leveraging the power of scikit-learn Perform supervised and unsupervised learning with ease, and evaluate the performanc
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 ntroducing 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-learr Supervised versus unsupervised 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 ntroduction 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 dy How to do it How it works Creating binary features through thresholding How to do it There's more Sparse matrices The fit method Working with categorical variables Getting read How to do it How it works There's more Dict Vectorizer class Imputing missing values through various strategies Ing 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 low 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 Reducing dimensionality with PCA How to do it low it work There's more Using factor analysis for decomposition Getting ready How it works Using kernel PCA for nonlinear dimensionality reduction etting ready How to do it How it works Using truncated Svd to reduce dimensionality How to do it How it works There's more Sign flipping Sparse matrices Using decomposition to classify with Dictionary Learning Getting read 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 Using ridge regression to overcome linear regression's shortfalls Getting ready How to do it Optimizing the ridge regression parameter Gettin How to do How it works There's more Bayesian ridge regression Using sparsity to regularize models How to do it How it works LASSO cross-validation- LASSOCV LASSO for feature selection Taking a more fundamental approach to regularization with LARS low 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 low it works Optimizing the number of centroids Getting ready How to do it low 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 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 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 low it works K-fold cross validation How to do it There's more Balanced cross-validation Getting ready How to do it There's more Cross-validation with Shuffle Split Getting ready How to do it Time series cross-validation Gettin How to do 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 read How to do it Clustering metrics Getting read How to do it Using dummy estimators to compare results Getting ready How to do it How it works Feature selection Getting read 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 etting ready Load the data Visualize the two classes

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baimaxishi 不太值,不是原版,价格还高
2018-09-21
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