training a dumb classifier

dumb_classifier.py

# Importing the dataset.
from sklearn.datasets import fetch_openml
mnist = fetch_openml('mnist_784', version=1)

# Creating independent and dependent variables.
X, y = mnist['data'], mnist['target']

# Splitting the data into training set and test set.
X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]

"""
The training set is already shuffled for us, which is good as this guarantees that all
cross-validation folds will be similar.
"""

# Training a binary classifier.
y_train_5 = (y_train == 5) # True for all 5s, False for all other digits.
y_test_5 = (y_test == 5)

"""
Building a dumb classifier that just classifies every single image in the “not-5” class.
"""

from sklearn.model_selection import cross_val_score
from sklearn.base import BaseEstimator
class Never5Classifier(BaseEstimator):
    def fit(self, X, y=None):
        pass
    def predict(self, X):
        return np.zeros((len(X), 1), dtype=bool)

never_5_clf = Never5Classifier()
cross_val_score(never_5_clf, X_train, y_train_5, cv=3, scoring="accuracy")