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# Generate Text Reports On Performance

## Preliminaries

```
/* Load libraries /*
from sklearn import datasets
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
```

## Load Iris Flower Data

```
/* Load data */
iris = datasets.load_iris()
/* Create feature matrix */
X = iris.data
/* Create target vector */
y = iris.target
/* Create list of target class names */
class_names = iris.target_names
```

## Create Training And Test Sets

```
/* Create training and test set */
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)
```

## Train A Logistic Regression Model

```
/* Create logistic regression */
classifier = LogisticRegression()
/* Train model and make predictions */
y_hat = classifier.fit(X_train, y_train).predict(X_test)
```

## Generate Report

```
/* Create a classification report */
print(classification_report(y_test, y_hat, target_names=class_names))
```

```
precision recall f1-score support
setosa 1.00 1.00 1.00 13
versicolor 1.00 0.62 0.77 16
virginica 0.60 1.00 0.75 9
avg / total 0.91 0.84 0.84 38
```

Note: Support refers to the number of observations in each class.

# Python Example for Beginners

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There are two sides to machine learning:

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