hyperparameter tuning

超参数调整是指在机器学习模型中调整超参数以提高模型性能的过程。超参数是在训练模型之前设置的参数，例如学习率、批量大小、正则化参数等。通过调整这些超参数，可以优化模型的性能并提高其准确性。超参数调整通常是一个迭代过程，需要不断地尝试不同的超参数组合，以找到最佳的超参数组合。

相关问题

sklearn.model_selection

sklearn.model_selection is a module in scikit-learn library which provides various tools for model selection and evaluation. It contains functions for splitting data into train and test sets, cross-validation, and grid search for hyperparameter tuning.

Some of the commonly used functions in sklearn.model_selection are:

1. train_test_split(): Splits data into random train and test subsets
2. KFold(): Splits data into k-folds for cross-validation
3. GridSearchCV(): Performs grid search over specified parameter values for hyperparameter tuning
4. RandomizedSearchCV(): Performs randomized search over specified parameter values for hyperparameter tuning
5. StratifiedKFold(): Splits data into k-folds while preserving the class distribution
6. TimeSeriesSplit(): Splits time-series data into train and test sets while preserving temporal order

Overall, sklearn.model_selection is an important module for developing and evaluating machine learning models.

python orion-ml

As an AI language model, I don't have personal preferences. However, I can provide information on Orion-ML.

Orion-ML is an open-source machine learning platform that allows developers to build, train, and deploy machine learning models. It provides a flexible and scalable architecture for building custom machine learning models using Python. Orion-ML includes a wide range of algorithms and tools for data preprocessing, model training, and evaluation.

Some of the key features of Orion-ML include:

1. Data preprocessing: Orion-ML provides tools for cleaning, transforming, and normalizing data before it is used to train a machine learning model.

2. Algorithm selection: Orion-ML supports a wide range of machine learning algorithms, including linear regression, decision trees, random forests, and neural networks.

3. Model training and evaluation: Orion-ML provides tools for training and evaluating machine learning models, including cross-validation and hyperparameter tuning.

4. Model deployment: Orion-ML allows developers to deploy machine learning models as RESTful APIs, making it easy to integrate them into other applications.

Overall, Orion-ML is a powerful and flexible machine learning platform that can help developers build and deploy machine learning models quickly and efficiently.