Naïve Bayes Classifier Algorithm Theorem Explained in Detail
In this ML Algorithms course tutorial, we are going to learn “Naïve Bayes Classifier in detail. we covered it by practically and theoretical intuition.
- What is the naive Bayes classifier algorithm?
- What is Naïve Bayes Classifier used for?
- How do Naïve Bayes Classifier work?
- What is Naïve in Naïve Bayes Classifier?
- Maths behind Naïve Bayes Classifier?
- What are the advantages & disadvantages of naive Bayes classifier?
- How to implement Naïve Bayes Classifier in python using sklearn?

Naïve Bayes Classifier Project
""" ## Naive Bayes Classifier ### Import Libraries """ # import libraries import numpy as np import pandas as pd """### Load Dataset""" #load dataset from sklearn.datasets import load_breast_cancer data = load_breast_cancer() data.data data.feature_names data.target data.target_names # create dtaframe df = pd.DataFrame(np.c_[data.data, data.target], columns=[list(data.feature_names)+['target']]) df.head() df.tail() df.shape """### Split Data""" X = df.iloc[:, 0:-1] y = df.iloc[:, -1] from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=2020) print('Shape of X_train = ', X_train.shape) print('Shape of y_train = ', y_train.shape) print('Shape of X_test = ', X_test.shape) print('Shape of y_test = ', y_test.shape) """## Train Naive Bayes Classifier Model""" from sklearn.naive_bayes import GaussianNB classifier = GaussianNB() classifier.fit(X_train, y_train) classifier.score(X_test, y_test) from sklearn.naive_bayes import MultinomialNB classifier_m = MultinomialNB() classifier_m.fit(X_train, y_train) classifier_m.score(X_test, y_test) from sklearn.naive_bayes import BernoulliNB classifier_b = BernoulliNB() classifier_b.fit(X_train, y_train) classifier_b.score(X_test, y_test) """## Predict Cancer""" patient1 = [17.99, 10.38, 122.8, 1001.0, 0.1184, 0.2776, 0.3001, 0.1471, 0.2419, 0.07871, 1.095, 0.9053, 8.589, 153.4, 0.006399, 0.04904, 0.05373, 0.01587, 0.03003, 0.006193, 25.38, 17.33, 184.6, 2019.0, 0.1622, 0.6656, 0.7119, 0.2654, 0.4601, 0.1189] patient1 = np.array([patient1]) patient1 classifier.predict(patient1) data.target_names pred = classifier.predict(patient1) if pred[0] == 0: print('Patient has Cancer (malignant tumor)') else: print('Patient has no Cancer (malignant benign)') """Ab milenge next tutorial me,Tab tak ke liye SIKHATE SIKHATE kuch IMPLEMENT karte raho, Thank You.....-:)"""