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Add pep8-naming to pre-commit hooks and fixes incorrect naming conventions (#7062)

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* ci(pre-commit): Add pep8-naming to `pre-commit` hooks (#7038)

* refactor: Fix naming conventions (#7038)

* Update arithmetic_analysis/lu_decomposition.py

Co-authored-by: Christian Clauss <cclauss@me.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* refactor(lu_decomposition): Replace `NDArray` with `ArrayLike` (#7038)

* chore: Fix naming conventions in doctests (#7038)

* fix: Temporarily disable project euler problem 104 (#7069)

* chore: Fix naming conventions in doctests (#7038)

Co-authored-by: Christian Clauss <cclauss@me.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Caeden
2022-10-12 23:54:20 +01:00
committed by GitHub
parent e2cd982b11
commit 07e991d553
140 changed files with 1552 additions and 1536 deletions
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14
machine_learning/gradient_boosting_regressor.py
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@ -26,25 +26,25 @@ def main():
print(df_boston.describe().T)
# Feature selection
X = df_boston.iloc[:, :-1]
x = df_boston.iloc[:, :-1]
y = df_boston.iloc[:, -1] # target variable
# split the data with 75% train and 25% test sets.
X_train, X_test, y_train, y_test = train_test_split(
X, y, random_state=0, test_size=0.25
x_train, x_test, y_train, y_test = train_test_split(
x, y, random_state=0, test_size=0.25
)
model = GradientBoostingRegressor(
n_estimators=500, max_depth=5, min_samples_split=4, learning_rate=0.01
)
# training the model
model.fit(X_train, y_train)
model.fit(x_train, y_train)
# to see how good the model fit the data
training_score = model.score(X_train, y_train).round(3)
test_score = model.score(X_test, y_test).round(3)
training_score = model.score(x_train, y_train).round(3)
test_score = model.score(x_test, y_test).round(3)
print("Training score of GradientBoosting is :", training_score)
print("The test score of GradientBoosting is :", test_score)
# Let us evaluation the model by finding the errors
y_pred = model.predict(X_test)
y_pred = model.predict(x_test)
# The mean squared error
print(f"Mean squared error: {mean_squared_error(y_test, y_pred):.2f}")