Fix ValueError: Inconsistent Input Samples

Python developers often face the ValueError: Found input variables with inconsistent numbers of samples. This error occurs when input data shapes don't match.

What Causes This Error?

The error appears when using machine learning libraries like scikit-learn. It happens when input arrays have different lengths.

For example, your feature matrix and target variable might have mismatched dimensions. The fit() method requires consistent sample sizes.

Common Scenarios

Here are typical cases that trigger this error:

1. Training data and labels have different lengths

2. Missing values in one array but not another

3. Incorrect data splitting during preprocessing

How to Fix the Error

Check your data shapes first. Use shape or len() to verify dimensions match.

import numpy as np
from sklearn.model_selection import train_test_split

X = np.array([[1, 2], [3, 4], [5, 6]])
y = np.array([1, 2])  # Different length

print(X.shape)  # (3, 2)
print(y.shape)  # (2,)

ValueError: Found input variables with inconsistent numbers of samples

Solution 1: Align Data Dimensions

Ensure all arrays have the same number of samples. Remove or add data points as needed.

# Fix by matching lengths
y = np.array([1, 2, 3])  # Now matches X

print(X.shape)  # (3, 2)
print(y.shape)  # (3,)

Solution 2: Check Data Splitting

When using train_test_split, ensure proper assignment. A similar issue occurs with x and y dimension mismatch.

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

Solution 3: Handle Missing Values

Clean your data first. Remove or impute missing values consistently. See NaN or Infinity errors for more details.

Prevention Tips

Always validate data shapes before model training. Use assertions in your code.

assert len(X) == len(y), "Input dimensions must match"

This catches mismatches early. Similar checks help with broadcast errors.

Conclusion

The ValueError for inconsistent samples is common but easy to fix. Always check data dimensions before model training. Clean and validate your data properly.

Remember to match array lengths and handle missing values. These steps will prevent most dimension-related errors in Python.