CODEWITHSUNDEEP
pythonscikit-learn
Nate
Nate

Reputation: 536

Different values each time I run the code even with random_state

Each time I run this code, I get a different value for the print statement. I'm confused why it's doing that because I specifically included the random_state parameter for the train/test split. (On a side note, I hope I'm supposed to encode the data; it was giving "ValueError: could not convert string to float" otherwise).

df = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/car/car.data',
                 names=['buying', 'maint', 'doors', 'persons',
                        'lug_boot', 'safety', 'acceptability'])

# turns variables into numbers (algorithms won't let you do it otherwise)
df = df.apply(LabelEncoder().fit_transform)
print(df)

X = df.reindex(columns=['buying', 'maint', 'doors', 'persons',
                        'lug_boot', 'safety'])
y = df['acceptability']


X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

print(X_train)

# decision trees classification
clf = tree.DecisionTreeClassifier(criterion='entropy')
clf = clf.fit(X_train, y_train)

y_true = y_test
y_pred = clf.predict(X_test)

print(math.sqrt(mean_squared_error(y_true, y_pred)))

Upvotes: 1

Views: 253

Answers (1)

EdChum
EdChum

Reputation: 394041

DecisionTreeClassifier also takes a random_state param: http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html

All you did was ensure that the train/test splits are repeatable but the classifier also needs to ensure it's own seed is the same on each run

Update

Thanks to @Chester VonWinchester for pointing out: https://github.com/scikit-learn/scikit-learn/issues/8443 due to sklearn's implementation choice it can be non-deterministic with max_features= None even though it should mean that all features are considered.

There is further information and discussion in the link above.

Upvotes: 1

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