Create Dummy Data for Classification
Classify Dummy Data
Breakdown of Metrics Included in Classification Report
List of Other Classification Metrics Available in sklearn.metrics

1. Create Dummy Data for Classification



In [1]:

    
import seaborn as sns 
import matplotlib.pyplot as plt
%matplotlib inline

from sklearn.datasets import make_blobs

data, labels = make_blobs(n_samples=100, n_features=2, centers=2,cluster_std=4,random_state=2)

plt.scatter(data[:,0], data[:,1], c = labels, cmap='coolwarm');

2. Classify Data



In [2]:

    
#Import LinearSVC
from sklearn.svm import LinearSVC

#Create instance of Support Vector Classifier
svc = LinearSVC()

#Fit estimator to 70% of the data
svc.fit(data[:70], labels[:70])

#Predict final 30%
y_pred = svc.predict(data[70:])

#Establish true y values
y_true = labels[70:]

3. Breakdown of Metrics Included in Classification Report

Precision Score

TP - True Positives
FP - False Positives

Precision - Accuracy of positive predictions.
Precision = TP/(TP + FP)



In [3]:

    
from sklearn.metrics import precision_score

print("Precision score: {}".format(precision_score(y_true,y_pred)))









    



Precision score: 0.9285714285714286

Recall Score

FN - False Negatives

Recall (aka sensitivity or true positive rate): Fraction of positives That were correctly identified.
Recall = TP/(TP+FN)



In [4]:

    
from sklearn.metrics import recall_score

print("Recall score: {}".format(recall_score(y_true,y_pred)))









    



Recall score: 0.8666666666666667

F1 Score

F1 Score (aka F-Score or F-Measure) - A helpful metric for comparing two classifiers. F1 Score takes into account precision and the recall. It is created by finding the the harmonic mean of precision and recall.

F1 = 2 x (precision x recall)/(precision + recall)



In [5]:

    
from sklearn.metrics import f1_score

print("F1 Score: {}".format(f1_score(y_true,y_pred)))









    



F1 Score: 0.896551724137931

Classification Report

Report which includes Precision, Recall and F1-Score.



In [6]:

    
from sklearn.metrics import classification_report

print(classification_report(y_true,y_pred))









    



             precision    recall  f1-score   support

          0       0.88      0.93      0.90        15
          1       0.93      0.87      0.90        15

avg / total       0.90      0.90      0.90        30

Confusion Matrix

Confusion matrix allows you to look at the particular misclassified examples yourself and perform any further calculations as desired.



In [7]:

    
from sklearn.metrics import confusion_matrix
import pandas as pd

confusion_df = pd.DataFrame(confusion_matrix(y_true,y_pred),
             columns=["Predicted Class " + str(class_name) for class_name in [0,1]],
             index = ["Class " + str(class_name) for class_name in [0,1]])

print(confusion_df)









    



         Predicted Class 0  Predicted Class 1
Class 0                 14                  1
Class 1                  2                 13

4. List of Other Classification Metrics Available in sklearn.metrics

accuracy_score
auc
average_precision_score
brier_score_loss
cohen_kappa_score
dcg_score
fbeta_score
hamming_loss
hinge_loss
jaccard_similarity_score
log_loss
matthews_corrcoef
ndcg_score
precision_recall_curve
precision_recall_fscore_support
roc_auc_score
roc_curve
zero_one_loss

sklearn.metrics also offers Regression Metrics, Model Selection Scorer, Multilabel ranking metrics, Clustering Metrics, Biclustering metrics, and Pairwise metrics.