ValueError when fitting model: Expected 2D array, got 1D array instead

Question

import numpy as np
from sklearn.linear_model import LinearRegression
X = np.array([1, 2, 3, 4, 5]) # Features
y = np.array([2, 4, 6, 8, 10]) # Target
model = LinearRegression()
model.fit(X, y) # <-- Error occurs here

When I try to fit my LinearRegression model, I get the following error:

ValueError: Expected 2D array, got 1D array instead:
array=[1. 2. 3. 4. 5.].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature
or array.reshape(1, -1) if it contains a single sample.

I understand the error is about the input shape, but I’m not sure why it happens here and what’s the proper way to fix it.

Why does scikit-learn expect a 2D array for X?

What’s the correct way to reshape the data in this case?

• Bending Rodriguez: did this answer your question?

Answer 1

use .reshape(-1, 1)

import numpy as np
from sklearn.linear_model import LinearRegression
X = np.array([1, 2, 3, 4, 5]).reshape(-1, 1) # Features
y = np.array([2, 4, 6, 8, 10]).reshape(-1, 1) # Target
model = LinearRegression()
model.fit(X, y)
print("Coefficients:", model.coef_)
print("Intercept:", model.intercept_)

If you have a one dimensional array this step is mandatory.

result:

> Coefficients: [[2.]]
> Intercept: [-1.77635684e-15]

Answer 2

Answering for the question "why":

Scikit-learn's LinearRegression expects the input features (X) to be a 2D array of shape (n_samples, n_features), even for a simple linear regression with one feature (i.e., a single x variable predicting y). This is because scikit-learn is designed to handle multiple features (multivariate regression), so the input must always be 2D.

Because the model.fit() API is designed for general use case, not just one dimension X and one dimension Y prediction, the API is more difficult to use for its simplest use case.