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# Hyperparameter Tuning Using Random Search

## Preliminaries

```
/* Load libraries */
from scipy.stats import uniform
from sklearn import linear_model, datasets
from sklearn.model_selection import RandomizedSearchCV
```

## Load Iris Dataset

```
/* Load data */
iris = datasets.load_iris()
X = iris.data
y = iris.target
```

## Create Logistic Regression

```
/* Create logistic regression */
logistic = linear_model.LogisticRegression()
```

## Create Hyperparameter Search Space

```
/* Create regularization penalty space */
penalty = ['l1', 'l2']
/* Create regularization hyperparameter distribution using uniform distribution */
C = uniform(loc=0, scale=4)
/* Create hyperparameter options */
hyperparameters = dict(C=C, penalty=penalty)
```

## Create Random Search

```
/* Create randomized search 5-fold cross validation and 100 iterations */
clf = RandomizedSearchCV(logistic, hyperparameters, random_state=1, n_iter=100, cv=5, verbose=0, n_jobs=-1)
```

## Conduct Random Search

```
/* Fit randomized search */
best_model = clf.fit(X, y)
```

## View Hyperparameter Values Of Best Model

```
/* View best hyperparameters */
print('Best Penalty:', best_model.best_estimator_.get_params()['penalty'])
print('Best C:', best_model.best_estimator_.get_params()['C'])
```

```
Best Penalty: l1
Best C: 1.66808801881
```

## Predict Using Best Model

```
/* Predict target vector */
best_model.predict(X)
```

```
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])
```

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