human
I am working on the file "HandsOnML/code/03_classification.ipynb".
The first task is:
Binary classifier
Can you write Python code for it?

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Give me a the Python code you requested?
assistant
#Here is the Python code you requested.

y_train_5 = (y_train == 5)
y_test_5 = (y_test == 5)

from sklearn.linear_model import SGDClassifier

sgd_clf = SGDClassifier(random_state=42)
sgd_clf.fit(X_train, y_train_5)

sgd_clf.predict([some_digit])

from sklearn.model_selection import cross_val_score
cross_val_score(sgd_clf, X_train, y_train_5, cv=3, scoring="accuracy")

from sklearn.model_selection import StratifiedKFold
from sklearn.base import clone

skfolds = StratifiedKFold(n_splits=3, random_state=42)

for train_index, test_index in skfolds.split(X_train, y_train_5):
    clone_clf = clone(sgd_clf)
    X_train_folds = X_train[train_index]
    y_train_folds = (y_train_5[train_index])
    X_test_fold = X_train[test_index]
    y_test_fold = (y_train_5[test_index])

    clone_clf.fit(X_train_folds, y_train_folds)
    y_pred = clone_clf.predict(X_test_fold)
    n_correct = sum(y_pred == y_test_fold)
    print(n_correct / len(y_pred))

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")

from sklearn.model_selection import cross_val_predict

y_train_pred = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3)

from sklearn.metrics import confusion_matrix

confusion_matrix(y_train_5, y_train_pred)

y_train_perfect_predictions = y_train_5

confusion_matrix(y_train_5, y_train_perfect_predictions)

from sklearn.metrics import precision_score, recall_score

precision_score(y_train_5, y_train_pred)

4344 / (4344 + 1307)

recall_score(y_train_5, y_train_pred)

4344 / (4344 + 1077)

from sklearn.metrics import f1_score
f1_score(y_train_5, y_train_pred)

4344 / (4344 + (1077 + 1307)/2)

y_scores = sgd_clf.decision_function([some_digit])
y_scores

threshold = 0
y_some_digit_pred = (y_scores > threshold)

y_some_digit_pred

threshold = 200000
y_some_digit_pred = (y_scores > threshold)
y_some_digit_pred

y_scores =...