Chapter 5. Model Evaluation and Improvement

Having discussed the fundamentals of supervised and unsupervised learning, and having explored a variety of machine learning algorithms, we will now dive more deeply into evaluating models and selecting parameters.

We will focus on the supervised methods, regression and classification, as evaluating and selecting models in unsupervised learning is often a very qualitative process (as we saw in Chapter 3).

To evaluate our supervised models, so far we have split our dataset into a training set and a test set using the train_test_split function, built a model on the training set by calling the fit method, and evaluated it on the test set using the score method, which for classification computes the fraction of correctly classified samples. Here’s an example of that process:

In[1]:

from sklearn.datasets import make_blobs
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split

# create a synthetic dataset
X, y = make_blobs(random_state=0)
# split data and labels into a training and a test set
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
# instantiate a model and fit it to the training set
logreg = LogisticRegression().fit(X_train, y_train)
# evaluate the model on the test set
print("Test set score: {:.2f}".format(logreg.score(X_test, y_test)))

Out[1]:

Test set score: 0.88

Remember, the reason we split our data into training and test sets is that we are interested ...

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