Remove Cross Validation Remove Decision Trees Remove Hypothesis Testing
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Statistical Modeling: Types and Components

Pickl AI

This is especially useful in finance and weather forecasting, where predictions guide decision-making. Hypothesis Testing : Statistical Models help test hypotheses by analysing relationships between variables. Techniques like linear regression, time series analysis, and decision trees are examples of predictive models.

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Top 10 Data Science Interviews Questions and Expert Answers

Pickl AI

Statistical Concepts A strong understanding of statistical concepts, including probability, hypothesis testing, regression analysis, and experimental design, is paramount in Data Science roles. It forms the basis for many statistical tests and estimators used in hypothesis testing and confidence interval estimation.

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Basic Data Science Terms Every Data Analyst Should Know

Pickl AI

Cross-Validation: A model evaluation technique that assesses how well a model will generalise to an independent dataset. Decision Trees: A supervised learning algorithm that creates a tree-like model of decisions and their possible consequences, used for both classification and regression tasks.

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[Updated] 100+ Top Data Science Interview Questions

Mlearning.ai

Decision trees are more prone to overfitting. Underfitting: Here, the model is so simple that it is not able to identify the correct relationship in the data, and hence it does not perform well even on the test data. Some algorithms that have low bias are Decision Trees, SVM, etc. character) is underlined or not.

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Big Data Syllabus: A Comprehensive Overview

Pickl AI

Statistical Analysis Introducing statistical methods and techniques for analysing data, including hypothesis testing, regression analysis, and descriptive statistics. Model Evaluation Techniques for evaluating machine learning models, including cross-validation, confusion matrix, and performance metrics.

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Top 50+ Data Analyst Interview Questions & Answers

Pickl AI

Techniques such as cross-validation, regularisation , and feature selection can prevent overfitting. What are the advantages and disadvantages of decision trees ? Overfitting occurs when a model learns the training data too well, including noise and irrelevant patterns, leading to poor performance on unseen data.