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Assignment 5 - Data Analytics and Model Evaluation

  1. Discuss Holdout method and random sampling methods.Discuss Holdout method and random sampling methods.Certainly, here's a concise summary of each method in a pointwise format: Holdout Method Purpose**: Evaluate machine learning model performance on unseen data. Process**: Split dataset into two subsets - approximately 70% training and 30% testing. Evaluation**: Test the model on the testing set after training on the training set. Advantages**: * Simple to implement. * Faster and less computationally demanding. Disadvantages**: * Inefficient data usage, especially with small datasets. *
  2. Write Short note onWrite Short note ona. Confusion Matrix b. AVC-ROC curve. Confusion Matrix Summary 1. Definition: A table showing the performance of a classification model. 1. Components: * True Positives (TP): Correct positive predictions. * True Negatives (TN): Correct negative predictions. * False Positives (FP): Incorrect positive predictions. * False Negatives (FN): Incorrect negative predictions. 1. Purpose: Helps calculate key metrics like accuracy, precision, recall, and F1-score. 1. Utility: Essential for ev
  3. Explain text analysis with all its steps.Explain text analysis with all its steps.Certainly! Here's a concise summary of text analysis in a pointwise format: 1. Data Collection * Gather text from sources like books, articles, and social media. 2. Preprocessing Tokenization**: Break text into words or phrases. Stop Words Removal**: Remove common words. Lowercasing**: Standardize text to lowercase. Stemming and Lemmatization**: Reduce words to their base forms. Part-of-Speech Tagging**: Assign grammatical roles to words. 3. Feature Extraction Bag of Words (BoW)**: Represe
  4. What is clustering? With suitable example explain the steps involved in K-means algorithm.