# Robust linear model estimation using RANSAC¶

In this example we see how to robustly fit a linear model to faulty data using the RANSAC algorithm.

Script output:

```Estimated coefficients (true, normal, RANSAC):
82.1903908408 [ 54.17236387] [ 82.08533159]
```

Python source code: `plot_ransac.py`

```import numpy as np
from matplotlib import pyplot as plt

from sklearn import linear_model, datasets

n_samples = 1000
n_outliers = 50

X, y, coef = datasets.make_regression(n_samples=n_samples, n_features=1,
n_informative=1, noise=10,
coef=True, random_state=0)

np.random.seed(0)
X[:n_outliers] = 3 + 0.5 * np.random.normal(size=(n_outliers, 1))
y[:n_outliers] = -3 + 10 * np.random.normal(size=n_outliers)

# Fit line using all data
model = linear_model.LinearRegression()
model.fit(X, y)

# Robustly fit linear model with RANSAC algorithm
model_ransac = linear_model.RANSACRegressor(linear_model.LinearRegression())
model_ransac.fit(X, y)

# Predict data of estimated models
line_X = np.arange(-5, 5)
line_y = model.predict(line_X[:, np.newaxis])
line_y_ransac = model_ransac.predict(line_X[:, np.newaxis])

# Compare estimated coefficients
print("Estimated coefficients (true, normal, RANSAC):")
print(coef, model.coef_, model_ransac.estimator_.coef_)