JuniorTheoryCommon

What happens when this program runs?

Python

import numpy as np
from sklearn.linear_model import LinearRegression

x = np.array([1, 2, 3, 4])
y = np.array([3, 5, 7, 9])
model = LinearRegression().fit(x, y)
print(model.coef_)

fit raises ValueError: it needs a 2-D X shaped (n_samples, n_features)

Expecting scikit-learn to reshape a 1-D x itself: fit refuses it with ValueError: Expected 2D array; pass x.reshape(-1, 1), one column with one row per sample.
Expecting a TypeError over integer data: scikit-learn converts int arrays to float on its own; the only problem here is the missing second dimension of X.

What shapes do x.reshape(-1, 1) and x.reshape(1, -1) give here, and which one does fit need?
Why may the target y stay 1-D while the feature matrix X must be 2-D?

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JuniorTheoryCommon

What happens when this program runs?

Python

import numpy as np
from sklearn.linear_model import LinearRegression

x = np.array([1, 2, 3, 4])
y = np.array([3, 5, 7, 9])
model = LinearRegression().fit(x, y)
print(model.coef_)

fit raises ValueError: it needs a 2-D X shaped (n_samples, n_features)

Expecting scikit-learn to reshape a 1-D x itself: fit refuses it with ValueError: Expected 2D array; pass x.reshape(-1, 1), one column with one row per sample.
Expecting a TypeError over integer data: scikit-learn converts int arrays to float on its own; the only problem here is the missing second dimension of X.

What shapes do x.reshape(-1, 1) and x.reshape(1, -1) give here, and which one does fit need?
Why may the target y stay 1-D while the feature matrix X must be 2-D?

Back to questions Open topic in roadmap →Answer it yourself — Quiz