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What is Model Evaluation and Selection

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Understanding the Model Evaluation and Selection  Techniques Content of  Model Evaluation •     Model Performance Metrics •     Cross-Validation Techniques •      Hyperparameter Tuning •      Model Selection Techniques Model Evaluation and Selection: Model evaluation and selection is the process of choosing the best machine learning model based on its performance on a given dataset. There are several techniques for evaluating and selecting machine learning models, including performance metrics, cross-validation techniques, hyperparameter tuning, and model selection techniques.     Performance Metrics: Performance metrics are used to evaluate the performance of a machine learning model. The choice of performance metric depends on the specific task and the type of machine learning model being used. Some common performance metrics include accuracy, precision, recall, F1 score, ROC curve, and AUC score. Cross-Validation Techniques: Cross-validation is a technique used to evaluate the per
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What is the Probability and Statistics

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Undrstand the Probability and Statistics in Data Science Contents of P robability and Statistics Probability Basics Random Variables and Probability Distributions Statistical Inference (Hypothesis Testing, Confidence Intervals) Regression Analysis Probability Basics Solution :  Sample Space = {H, T} (where H stands for Head and T stands for Tail) Solution :  The sample space is {1, 2, 3, 4, 5, 6}. Each outcome is equally likely, so the probability distribution is: Hypothesis testing involves making a decision about a population parameter based on sample data. The null hypothesis (H0) is the hypothesis that there is no significant difference between a set of population parameters and a set of observed sample data. The alternative hypothesis (Ha) is the hypothesis that there is a significant difference between a set of population parameters and a set of observed sample data. The hypothesis testing process involves the following steps: Formulate the null and alternative hypo
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Interview Questions and Answers

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Data Science  Questions and Answers Questions and Answers What is data science? Ans: In the interdisciplinary subject of data science, knowledge and insights are derived from data utilizing scientific methods, procedures, algorithms, and systems. What are the steps involved in the data science process? Ans : The data science process typically involves defining the problem, collecting and cleaning data, exploring the data, developing models, testing and refining the models, and presenting the results. What is data mining? Ans: Data mining is the process of discovering patterns in large datasets through statistical methods and machine learning. What is machine learning? Ans : Machine learning is a subset of artificial intelligence that involves using algorithms to automatically learn from data without being explicitly programmed. What kinds of machine learning are there? Ans : The different types of machine learning are supervised learning, unsupervised learning, semi-supervised learni
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