How to train a machine learning model?ΒΆ

The Machine Learning library scikit-learn contains methods for:

  • Supervised Learning (regression and classification, e.g. logistic regression, Support Vector Machines, Random Forests)

  • Unsupervised Learning (clustering, PCA)

  • Preprocessing (scaling, text vectorization)

  • Model validation

Install it with:

pip install scikit-learn

Here is an example training a straightforward model:


from sklearn import svm, datasets
from sklearn.model_selection import cross_val_score, train_test_split

# Load one of the example datasets
# and divide it into a training and test set:
iris = datasets.load_iris()
Xtrain, Xtest, Ytrain, Ytest = \
    train_test_split(iris.data, iris.target, \
    test_size=0.4, random_state=42)


# Fit a Support Vector Machine model and test it:
svc = svm.SVC(kernel='linear', C=1.0).fit(Xtrain, Ytrain)
print(svc.score(Xtest, Ytest))


# Do a five-fold cross-validation:
print(cross_val_score(svc, Xtrain, Ytrain, cv=5))

See also