Remove Cross Validation Remove Exploratory Data Analysis Remove Support Vector Machines
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Top 10 Data Science Interviews Questions and Expert Answers

Pickl AI

Machine Learning Algorithms Candidates should demonstrate proficiency in a variety of Machine Learning algorithms, including linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks. What is cross-validation, and why is it used in Machine Learning?

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Predicting Heart Failure Survival with Machine Learning Models — Part II

Towards AI

That post was dedicated to an exploratory data analysis while this post is geared towards building prediction models. In our exercise, we will try to deal with this imbalance by — Using a stratified k-fold cross-validation technique to make sure our model’s aggregate metrics are not too optimistic (meaning: too good to be true!)

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Artificial Intelligence Using Python: A Comprehensive Guide

Pickl AI

Data Normalization and Standardization: Scaling numerical data to a standard range to ensure fairness in model training. Exploratory Data Analysis (EDA) EDA is a crucial preliminary step in understanding the characteristics of the dataset. classification, regression) and data characteristics.

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Basic Data Science Terms Every Data Analyst Should Know

Pickl AI

Clustering: An unsupervised Machine Learning technique that groups similar data points based on their inherent similarities. Cross-Validation: A model evaluation technique that assesses how well a model will generalise to an independent dataset.