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



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from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
digits = load_digits()
X_train, X_test, y_train, y_test = train_test_split(
    digits.data, digits.target, random_state=0)



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X_train.shape



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np.bincount(y_train)

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()

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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!conda upgrade scikit-learn



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!pip install -U scikit-learn

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. Try sklearn.neighbors.KNeighborsClassifier for example.



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# %load solutions/train_iris.py