Metric for Hyperparameter Tuning
This hyperparameter tuning techniques, like grid search or Bayesian optimization, can be used to optimize the CatBoost model’s performance. This is a crucial step, the choice of the metric to use during herperparameter tuning depends on the nature of our problem and our specific goals.
Python3
import numpy as npfrom catboost import CatBoostClassifier, Pool, cvfrom sklearn.datasets import load_irisfrom sklearn.model_selection import train_test_split# Load the Iris datasetiris = load_iris()X, y = iris.data, iris.target# Split the data into training and testing setsX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)# Create a CatBoostClassifiermodel = CatBoostClassifier(iterations=100, learning_rate=0.1, depth=6, verbose=0)# Create a Pool object for the training datatrain_pool = Pool(X_train, label=y_train)# Define a parameter grid with hyperparameters to search overparam_grid = { 'iterations': [100, 200], 'learning_rate': [0.01, 0.1, 0.2], 'depth': [4, 6, 8],}# Perform grid search with cross-validationgrid_search_results = model.grid_search(param_grid, train_pool, cv=3, partition_random_seed=42, verbose=10)# Get the best hyperparametersbest_params = grid_search_results['params']print("Best Hyperparameters:")print(best_params) |
Output:
Best Hyperparameters:
{'depth': 4, 'iterations': 200, 'learning_rate': 0.1}
Grid search with cross-validation is a good way to find the best hyperparameters for your machine learning model. It works by trying out different combinations of hyperparameter values and evaluating the model on each combination. The best hyperparameters are the ones that produce the best model performance on the cross-validation folds.
CatBoost also provides a number of other metrics:
- Per-class metrics: Using this, accuracy, precision, F1 and recall can be calculated for each individual class in multiclass classification.
- Grouped metrics: Using this, metrics for different groups of data can be calculated.
- Custom metrics: Using this one can create own custom metrics using Python.
CatBoost metrics for model evaluation are invaluable tools that guide you in building high-performing and robust machine learning models. Whether you’re working on classification, regression, over-fitting detection, or hyperparameter tuning, the right choice of metrics allows you to assess and optimize your models effectively.
CatBoost Metrics for model evaluation
To make sure our model’s performance satisfies evolving expectations and criteria, proper evaluation is crucial when it comes to machine learning model construction. Yandex’s CatBoost is a potent gradient-boosting library that gives machine learning practitioners and data scientists a toolbox of measures for evaluating model performance.
Table of Content
- CatBoost
- CatBoost Metrics
- Metrics for Classification
- Metrics for Regression
- Metrics for Over-fitting Detection
- Metric for Hyperparameter Tuning