I am trying to create a probability for decision tree and multilayer perceptron. I am facing an issue in creating an aggregate method for each class using the above-mentioned classifiers. I want to know how to create a method for classes that are in the data set.
- here class means four classes which are given in the dataset
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import plot_confusion_matrix
from sklearn.naive_bayes import GaussianNB, MultinomialNB
from sklearn.metrics import accuracy_score
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import precision_score, recall_score,auc
from sklearn.metrics import roc_curve,roc_auc_score, plot_roc_curve
from sklearn.preprocessing import LabelEncoder
path="G:\Forest.xlsx"
rawdata= pd.read_excel(path)
print("data summary")
print(rawdata.describe())
nrow, ncol = rawdata.shape
X, y = rawdata.iloc[:, 1:].values, rawdata.iloc[:, 0].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, stratify=y, random_state=0)
clf = DecisionTreeClassifier(criterion='entropy')
clf=clf.fit(X_train, y_train)
tar_pred = clf.predict(X_test)
prob = clf.predict_proba(X_test)
#print("Accuracy score of our model with Decision Tree:",accuracy_score(y_test, tar_pred))
#precision = precision_score(y_true=y_test, y_pred=tar_pred, average='micro')
#print("Precision score of our model with Decision Tree :", precision)
#recall = recall_score(y_true=y_test, y_pred=tar_pred, average='micro')
#print("Recall score of our model with Decision Tree :", recall)
print("probability:",prob)
print("Accuracy score of our model with DT :", accuracy_score(y_test, tar_pred))
scores = cross_val_score(clf, X, y, cv=10)
print("Accuracy score of our model with DT under cross validation :", scores.mean())
print('Mean Accuracy of DT: %.3f%%' % (sum(scores)/float(len(scores))))
clf_MLP = MLPClassifier(hidden_layer_sizes=(5, 2), activation='logistic', solver='sgd', learning_rate='constant', learning_rate_init=0.1)
clf_MLP.fit(X_train, np.ravel(y_train, order='C'))
predictions = clf_MLP.predict(X_test)
pro=clf_MLP.predict_proba(X_test)
print("Accuracy score of our model with MLP :", accuracy_score(y_test, predictions))
scores = cross_val_score(clf_MLP, X, y, cv=10)
print("Accuracy score of our model with MLP under cross validation :", scores.mean())
print("probability:",pro)
print('Mean Accuracy Of MLP: %.3f%%' % (sum(scores)/float(len(scores))))[inline][python][python][/python][/python][/inline]
