Remove **constant (zero-variance) features** during feature engineering so your model doesn’t waste capacity on columns that never change. You’ll scan a 2D dataset and drop any feature whose values are identical across all samples.

Constant-feature detection can be defined as:
$$
\text{var}(x_j) = \frac{1}{n}\sum_{i=1}^{n}(x_{ij} - \mu_j)^2,\quad \text{drop feature } j \text{ if } \text{var}(x_j)=0
$$

## Requirements

Implement the function
```python
import numpy as np

def remove_constant_features(X):
    \"""
    Removes constant (zero-variance) features from a dataset.

    Args:
        X (np.ndarray): 2D data of shape (n_samples, n_features).

    Returns:
        tuple[np.ndarray, list[int]]:
            - X_filtered: The dataset with all constant columns removed.
            - kept_indices: The original feature indices that were kept (in order).
    \"""
    # Your solution below
```

**Rules:**
- A feature (column) is constant if all its values are exactly the same across all rows.
- Return the filtered dataset as a **NumPy array** (same row order), plus `kept_indices`.
- Do not use prebuilt feature-selection utilities (e.g., from `sklearn`).
- Use **NumPy** vectorized operations.
- Keep the scope simple: compute constant-ness directly from the input (no streaming, no file I/O).

## Example

```python
X = np.array([
    [1.0, 5.0, 9.0],
    [2.0, 5.0, 8.0],
    [3.0, 5.0, 7.0],
])

remove_constant_features(X)
```

Output:
```python
(
    array([
        [1.0, 9.0],
        [2.0, 8.0],
        [3.0, 7.0],
    ]),
    [0, 2]
)
```

Users by Platform

Hand of Straights

Removing constant features

Solve this easy level ML coding question from General. The premier LeetCode for ML platform to master Python, SQL, and Machine Learning algorithms.

288. Removing constant features

Examples

Constraints

Similar Questions