ml_classification_plot

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

User installation

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

pip install plotclassification==0.0.4

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.plot_probability_histogram()