Title: Generate Text Reports On Performance
Slug: generate_text_reports_on_performance
Summary: How to create generate a text report on a model's performance in scikit-learn for machine learning in Python.
Date: 2017-09-14 12:00
Category: Machine Learning
Tags: Model Evaluation Authors: Chris Albon

Preliminaries



In [7]:

    
# 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



In [8]:

    
# 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



In [9]:

    
# 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



In [10]:

    
# Create logistic regression
classifier = LogisticRegression()

# Train model and make predictions
y_hat = classifier.fit(X_train, y_train).predict(X_test)

Generate Report



In [11]:

    
# 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.