Machine Learning for Customer Value

Learn how to use machine learning to calculate customer value, predict behavior, and maximize the long-term value of your client base.

1. ML Principles for Customer Value

# Defining the Search Space

lr = trial.suggest_float('lr', 1e-5, 1e-1, log=True)

batch_size = trial.suggest_int('batch_size', 32, 256)

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Fundamentals of Optimization in ML

Hyperparameter optimization

Neural Architecture Search

Frequently asked questions

Does machine learning automate the search for optimal parameters?

Machine learning automates the search for optimal parameters, improving optimization strategies and enabling adaptation to specific problem characteristics, significantly reducing time and effort.

What ML methods are best for hyperparameter optimization?

Several ML methods are effective for hyperparameter optimization including Bayesian Optimization, Random Search, Grid Search, Evolutionary Algorithms and Tree-structured Parzen Estimators.

What is the difference between Bayesian Optimization, Random Search, and Grid Search?

Bayesian Optimization uses a probabilistic model to guide the search for optimal hyperparameters, while Random Search explores the parameter space randomly. Grid Search evaluates all possible combinations of hyperparameter values.

How do I choose an optimization method?

Choosing an optimization method depends on factors like the complexity of the problem and the size of the search space; Bayesian Optimization is often preferred for complex problems, while Random Search can be suitable for simpler cases.

▶ Try it live

Everything above runs in your browser — open Hash Function Avalanche Visualizer and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Hash Function Avalanche Visualizer simulation