Dr.Muttipati

 def candidate_elimination(attributes, target):     """     Implements the Candidate-Elimination algorithm for concept learning.     Parameters:     - attributes: List of examples (list of lists).     - target: List of target values (list of strings, e.g., 'Yes' or 'No').     Returns:     - S: Most specific boundary.     - G: Most general boundary.     """     # Step 1: Initialize S (most specific) and G (most general)     num_attributes = len(attributes[0])     S = ["Φ"] * num_attributes     G = [["?"] * num_attributes]     # Step 2: Process each training example     for i, example in enumerate(attributes):         if target[i] == "Yes":  # Positive example             # Remove inconsistent hypotheses from G             G = [g for g in G if is_consis...

 def find_s_algorithm(data, target):     """     Implements the Find-S algorithm for concept learning.     Parameters:     - data: List of examples (list of lists).     - target: List of target values (list of strings, e.g., 'Yes' or 'No').     Returns:     - The most specific hypothesis.     """     # Step 1: Initialize the most specific hypothesis     hypothesis = ["Φ"] * len(data[0])     # Step 2: Iterate over the dataset     for i, instance in enumerate(data):         if target[i] == "Yes":  # Process only positive examples             for j in range(len(instance)):                 if hypothesis[j] == "Φ":  # Initialize to the first positive example                     hypothesis[j] = instance[j]     ...

from sklearn.datasets import fetch_20newsgroups from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import train_test_split from sklearn.naive_bayes import MultinomialNB from sklearn.metrics import accuracy_score, classification_report # Step 1: Load a sample dataset (e.g., 20 Newsgroups) categories = ['sci.space', 'comp.graphics', 'rec.sport.baseball'] newsgroups = fetch_20newsgroups(subset='all', categories=categories) # Print information about the newsgroups dataset print("=== Newsgroups Dataset Information ===") print(f"Number of documents: {len(newsgroups.data)}") print(f"Number of categories: {len(newsgroups.target_names)}") print("Categories:", newsgroups.target_names) print("First document sample:\n", newsgroups.data[0][:500])  # Print first 500 characters of the first document print("\n") # Step 2: Preprocess the text data vectorizer = TfidfVectorize...

The Naive Bayes Classifier is a probabilistic machine learning model widely used for classification tasks, including document classification. Based on Bayes' Theorem, it assumes that the features (in this case, words or terms in the documents) are conditionally independent given the class label. Despite this "naive" assumption, it often performs well in practice, especially for text classification. Steps to Perform Document Classification Using Naive Bayes 1.  Prepare the Dataset Documents : Assume you have a set of documents, each labeled with a category (e.g., "Sports", "Politics", "Technology"). Preprocessing : Tokenize the text into words. Remove stop words (e.g., "the", "is", "and"). Perform stemming or lemmatization to reduce words to their base forms. Convert text into a numerical representation, such as a  bag-of-words  or  TF-IDF  vector. 2.  Split the Dataset Divide the dataset into a  training set  and...