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1. Since scikit-learn implements a highly optimized version of logistic regression that also supports multiclass settings off-the-shelf, we will skip the implementation and use the sklearn.linear_mod

In this section about ensemble methods, we discuss boosting with a special focus on its most common implementation, AdaBoost 。 1. Via the base_estimator attribute, we  train the AdaBoostClassifier

1. Create a more complex classifcation problem using the Wine dataset import pandas as pd df_wine = pd.read_csv('./datasets/wine/wine.data', header=None) # https://archive.

1. Compute the ROC curves from the test set to check if the MajorityVoteClassifier generalizes well to unseen data . from sklearn.metrics import roc_curve from sklearn.metrics import auc colors =

start with a warm-up exercise and implement a simple ensemble classifer for majority voting in Python. Although the following algorithm also generalizes to multi-class settings via plurality voting,

The goal behind ensemble methods is to combine different classifers into a meta-classifer that has a better generalization performance than each individual classifer alone。focus on the most popular

For our convenience, scikit-learn implements a kernel PCA class in the sklearn.decomposition submodule. The usage is similar to the standard PCA class, and we can specify the kernel via the kernel p

In this section, We learn how to project data points that were not part of the training dataset . 1. modify the rbf_kernel_pca function  returns the eigenvalues of the kernel matrix: from sc

Receiver operator characteristic (ROC) graphs are useful tools for selecting models for classifcation based on their performance with respect to the false positive and true positive rates, which are

Take a look at a kernelized version of PCA, or kernel PCA, which relates to the concepts of kernel SVM. Using kernel PCA, we will learn how to transform data that is not linearly separable onto a n

1.  Streamlining workflows with pipelines  1.1 Loading the Breast Cancer Wisconsin dataset from distutils.version import LooseVersion as Version from sklearn import __version__ as sklearn_vers

Linear Discriminant Analysis (LDA) can be used as a technique for feature extraction to increase the computational effciency and reduce the degree of over-ftting due to the curse of dimensionality i

1. Total and explained variance    1.1 process the Wine data into separate training and test sets import pandas as pd from sklearn.model_selection import train_test_split from sklearn.prep

1. Common solutions to reduce the generalization error are listed as follows : • Collect more training data • Introduce a penalty for complexity via regularization【L1、 L2】 • Choose a simple

1. Call function from sklearn.neighbors import KNeighborsClassifier knn = KNeighborsClassifier(n_neighbors=5, p=2, metric='minkowski') knn.fit(X_train_std, y_train) 2. Plt  result plot_de

1. Building a decision tree from sklearn.tree import DecisionTreeClassifier tree = DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=0) tree.fit(X_train, y_train) X_combined

1. Create a simple dataset that has the form of an XOR gate using the logical_xor function from NumPy . np.random.seed(0) X_xor = np.random.randn(200, 2) y_xor = np.logical_xor(X_xor[:, 0] > 0,

1. Train a SVM model : from sklearn.svm import SVC svm = SVC(kernel='linear', C=1.0, random_state=0) svm.fit(X_train_std, y_train) plot_decision_regions(X_combined_std, y_combined, classifier=svm

1. Obtaining the IMDb movie review dataset ： A compressed archive of the movie review dataset  ---- http://ai.stanford.edu/~amaas/data/sentiment/ import pandas as pd df = pd.read_csv('./datasets