# Handling Imbalanced Classes In Logistic Regression

## Preliminaries

```
/* Load libraries */
from sklearn.linear_model import LogisticRegression
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
import numpy as np
```

## Load Iris Flower Dataset

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

## Make Classes Imbalanced

```
/* Make class highly imbalanced by removing first 40 observations */
X = X[40:,:]
y = y[40:]
/* Create target vector indicating if class 0, otherwise 1 */
y = np.where((y == 0), 0, 1)
```

## Standardize Features

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

## Train A Logistic Regression With Weighted Classes

```
/* Create decision tree classifer object */
clf = LogisticRegression(random_state=0, class_weight='balanced')
/* Train model */
model = clf.fit(X_std, y)
```

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