scikit learn linear regression
from sklearn.linear_model import LinearRegression
X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
y = np.dot(X, np.array([1, 2])) + 3
reg = LinearRegression().fit(X, y)
reg.score(X, y)
reg.coef_
reg.intercept_
reg.predict(np.array([[3, 5]]))multinomial regression scikit learn
model1 = LogisticRegression(random_state=0, multi_class='multinomial', penalty='none', solver='newton-cg').fit(X_train, y_train)
preds = model1.predict(X_test)
#print the tunable parameters (They were not tuned in this example, everything kept as default)
params = model1.get_params()
print(params)
{'C': 1.0, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': None, 'penalty': 'none', 'random_state': 0, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}Copyright © 2021 Codeinu
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