The document discusses the process of selecting the best machine learning model, including evaluating multiple models on a holdout dataset to determine the best performing one. It notes common model types like logistic regression, decision trees, and neural networks that could be considered. It also addresses the need to split data into training, validation, and test sets to accurately assess model performance and avoid overfitting conclusions from multiple comparisons of models during selection.

1. Modelling step:
Model Selection
DataTalks.Club
Machine Learning Zoomcamp
Session #1.5

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Session #1.5: Plan
● Process for selecting the best model
● Evaluating the model
● Multiple comparison problem

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Modeling
Which model to choose?
● Logistic regression
● Decision tree
● Neural network
● Or many others

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Selecting the best model
Holdout + train

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Making predictions

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Scoring

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Multiple comparisons problem
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Validation & Test
Train + validation + test

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Scoring

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Model selection (6 steps)

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