# Ridge Regression

## Preliminaries

```
/* Load libraries */
from sklearn.linear_model import Ridge
from sklearn.datasets import load_boston
from sklearn.preprocessing import StandardScaler
```

## Load Boston Housing Dataset

```
/* Load data */
boston = load_boston()
X = boston.data
y = boston.target
```

## Standardize Features

```
/* Standarize features */
scaler = StandardScaler()
X_std = scaler.fit_transform(X)
```

## Fit Ridge Regression

The hyperparameter, α, lets us control how much we penalize the coefficients, with higher values of α creating simpler modelers. The ideal value of α should be tuned like any other hyperparameter. In scikit-learn, α is set using the `alpha`

parameter.

```
/* Create ridge regression with an alpha value */
regr = Ridge(alpha=0.5)
/* Fit the linear regression */
model = regr.fit(X_std, y)
```

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