plotclassification 0.0.4

This package perform different way to visualize machine learning and deep learning classification results

Plots of Classifier Performance :- When it comes to machine learning and deep learning task there are many ways to plot the performance of a classifier. There is metrics to compare different estimators like accuracy, precision, recall and f1 score.All of the common classification metrics are calculated from true positive, true negative, false positive and false negative incidents. The most popular plots are definitely ROC curve, PRC and the confusion matrix.

There are four ways to check if the predictions are right or wrong:

  1. TN / True Negative: the case was negative and predicted negative
  2. TP / True Positive: the case was positive and predicted positive
  3. FN / False Negative: the case was positive but predicted negative
  4. FP / False Positive: the case was negative but predicted positive

  Precision:- Accuracy of positive predictions  = TP/(TP + FP)
  Recall:- Fraction of positives that were correctly identified = TP/(TP+FN)
  F1 Score = 2*(Recall * Precision) / (Recall + Precision)

User installation :

If you already have a working installation of numpy and pandas, the easiest way to install plotly_ml_classification is using pip

pip install plotclassification

This package depend on other packages:

  1.numpy
  2.pandas
  3.sklearn 
  4.plotly

Usage

# import libraries
import plotclassification 
from sklearn import datasets 
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split 


# Load data
iris = datasets.load_iris()
# Create feature matrix
features = iris.data
# Create target vector 
target = iris.target

#create list of classname 
class_names = iris.target_names
class_names


# Create training and test set 
x_train, x_test, y_train, y_test = train_test_split(features,
                                                     target,
                                                     test_size=0.9, 
                                                     random_state=1)


# Create logistic regression 
classifier = LogisticRegression()

# Train model and make predictions
model = classifier.fit(x_train, y_train)

# create predicted probabilty matrix 
y_test__scores = model.predict_proba(x_test)

# initialize parameters value
plot=plotclassification.plot(y=y_test,
	         y_predict_proba=y_test__scores,
	         class_name=['Class 1','class 2','class 3'])

plot.class_name
['Class 1', 'class 2', 'class 3']

# classification report plot
plot.plot_classification_report()

#  confusion matrix plot
plot.plot_confusion_matrix()

# precision recall curve plot
plot.plot_precision_recall_curve()

# roc plot
plot.plot_roc()

# predicted probability histogram plot
plot_probability_histogram()

Github file source

Change Log

0.0.1 (06/03/2021)

• First Release

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