Why do we use the strange word "hyperparameter"? What makes it hyper? Here, you will understand what model parameters are, and why they are different from hyperparameters in machine learning. You will then see why we would want to tune them and how the default setting of caret automatically includes hyperparameter tuning.

Parameters vs hyperparameters

Model parameters vs. hyperparameters

Hyperparameters in linear models

What are the coefficients?

Recap of machine learning basics

Machine learning with caret

Resampling schemes

Hyperparameter tuning in caret

Hyperparameters in Stochastic Gradient Boosting

Changing the number of hyperparameters to tune

Tune hyperparameters manually

Introduction to hyperparameters

In this chapter, you will learn how to tune hyperparameters with a Cartesian grid. Then, you will implement faster and more efficient approaches. You will use Random Search and adaptive resampling to tune the parameter grid, in a way that concentrates on values in the neighborhood of the optimal settings. 

Finding hyperparameters

Cartesian grid search in caret

Plot hyperparameter model output

Grid vs. Random Search

Grid search with range of hyperparameters

Find train() option for random search

Random search with caret

Adaptive resampling

Advantages of Adaptive Resampling

Adaptive Resampling with caret

Hyperparameter tuning with caret

Here, you will use another package for machine learning that has very convenient hyperparameter tuning functions. You will define a Cartesian grid or perform Random Search, as well as advanced techniques. You will also learn different ways to plot and evaluate models with different hyperparameters.

Machine learning with mlr

Machine Learning with mlr

Modeling with mlr

Grid and random search with mlr

Random search with mlr

Perform hyperparameter tuning with mlr

Evaluating hyperparameters with mlr

Why to evaluate tuning?

Evaluating hyperparameter tuning results

Advanced tuning with mlr

Define advanced tuning controls

Define aggregated measures

Setting hyperparameters

Hyperparameter tuning with mlr

In this final chapter, you will use h2o, another package for machine learning with very convenient hyperparameter tuning functions. You will use it to train different models and define a Cartesian grid. Then, You will implement a Random Search use stopping criteria. Finally, you will learn AutoML, an  h2o interface which allows for very fast and convenient model and hyperparameter tuning with just one function.

h2o ile Machine Learning

h2o ile modelleme için veriyi hazırla

h2o ile modelleme

h2o ile grid ve rastgele arama

h2o ile grid search

h2o ile rastgele arama

Durdurma kıstasları

H2O ile otomatik Machine Learning

h2o'da AutoML

Lider tablosunu puanlama

h2o modellerini çıkar ve performansı değerlendir

Kapanış

Hyperparameter tuning with h2o

Bc test data

Bc train data

Breast cancer data

Breast cancer data orig

Datasets descriptions

Knowledge data

Knowledge orig

Knowledge test data

Knowledge train data

Seeds data

Seeds dataset

Seeds test data

Seeds train data

Voters data

Voters orig

Voters test data

Voters train data

Birçok Machine Learning probleminde, modeli doğrudan çalıştırıp bir tahmin almak yeterli değildir; en doğru tahmini yapan en iyi modeli istersin. Modelini mükemmelleştirmenin bir yolu hiperparametre ayarlamasıdır; bu da o modele özgü ayarların optimize edilmesi demektir. Bu kursta, caret, mlr ve h2o paketleriyle ızgara araması (grid search), rastgele arama (random search), uyarlamalı yeniden örnekleme (adaptive resampling) ve otomatik Machine Learning (AutoML) kullanarak hiperparametrelerin en iyi kombinasyonunu verimli şekilde bulacaksın. Ayrıca farklı veri kümeleriyle çalışacak ve rastgele ormanlar, gradient boosting makineleri, destek vektör makineleri ve hatta sinir ağları gibi farklı gözetimli öğrenme modellerini ayarlayacaksın. Hazır ol, ayarlamaya başlıyoruz!

Machine Learning with caret in R

Caret, mlr ve h2o paketleriyle grid search, random search, adaptif örnekleme ve otomatik ML kullanın.

R'de Hiperparametre Ayarlama

En iyi tahmin sonuçlarını elde etmek için modelinizin hiperparametrelerini nasıl ayarlayacağınızı öğrenin.

h2o ile Machine Learning

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