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%matplotlib nbagg
import matplotlib.pyplot as plt
import numpy as np

Get some data to play with



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from sklearn.datasets import load_digits
digits = load_digits()



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from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(digits.data,
                                                    digits.target)

Really Simple API

0) Import your model class



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from sklearn.svm import LinearSVC

1) Instantiate an object and set the parameters



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svm = LinearSVC(C=0.1)

2) Fit the model



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svm.fit(X_train, y_train)

3) Apply / evaluate



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print(svm.predict(X_train))
print(y_train)



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svm.score(X_train, y_train)



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svm.score(X_test, y_test)

And again



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from sklearn.ensemble import RandomForestClassifier



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rf = RandomForestClassifier(n_estimators=50)



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rf.fit(X_train, y_train)



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rf.score(X_test, y_test)



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%load https://raw.githubusercontent.com/scikit-learn/scikit-learn/master/examples/classification/plot_classifier_comparison.py

Exercises

Load the iris dataset from the sklearn.datasets module using the load_iris function.

Split it into training and test set using train_test_split. Then train an evaluate a classifier of your choice.



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# %load solutions/train_iris.py