Remove Cross Validation Remove Data Preparation Remove Support Vector Machines
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Predicting Heart Failure Survival with Machine Learning Models — Part II

Towards AI

(Check out the previous post to get a primer on the terms used) Outline Dealing with Class Imbalance Choosing a Machine Learning model Measures of Performance Data Preparation Stratified k-fold Cross-Validation Model Building Consolidating Results 1. Data Preparation Photo by Bonnie Kittle […]

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Artificial Intelligence Using Python: A Comprehensive Guide

Pickl AI

Data Preparation for AI Projects Data preparation is critical in any AI project, laying the foundation for accurate and reliable model outcomes. This section explores the essential steps in preparing data for AI applications, emphasising data quality’s active role in achieving successful AI models.

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Statistical Modeling: Types and Components

Pickl AI

Start by collecting data relevant to your problem, ensuring it’s diverse and representative. After collecting the data, focus on data cleaning, which includes handling missing values, correcting errors, and ensuring consistency. Data preparation also involves feature engineering.

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How to Choose MLOps Tools: In-Depth Guide for 2024

DagsHub

A traditional machine learning (ML) pipeline is a collection of various stages that include data collection, data preparation, model training and evaluation, hyperparameter tuning (if needed), model deployment and scaling, monitoring, security and compliance, and CI/CD.