Tensor reshaping changes an array’s dimensions without changing its element values. In a scikit-learn logistic-regression example, reshape(-1, 1) turns one-dimensional input data into a single column of observations. section 8.3
Giving x the shape the model expects
A toddler-meltdown dataset stores hours without a nap in a NumPy array. Applying .reshape(-1, 1) makes x a column, preventing the input-format error that would otherwise occur. The output y remains one-dimensional because scikit-learn treats it as one response variable. section 8.3
import numpy as np
from sklearn.linear_model import LogisticRegression
x = np.array([0.1, 0.5, 0.7, 1.2, 1.2, 1.5, 1.6, 1.9, 2.5, 2.5,
2.7, 3.2, 3.3, 3.7, 3.8, 4.1, 4.5, 4.5, 4.5, 5.0,
5.1, 5.6, 5.7, 6.2, 6.3]) \
.reshape(-1, 1) #1
y = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0,
0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0,
1.0, 1.0, 1.0, 1.0, 1.0]) #2
model = LogisticRegression(penalty=None) #3
model.fit(x, y) #3
b1 = model.coef_.flatten()[0] #4
b0 = model.intercept_[0] #4
print(f"β₀={b0:.4f}, β₁={b1:.4f}") #5
The listing shows reshaping applied directly to NumPy input before fitting LogisticRegression. section 8.3
