Understanding Training and Tuning

Training and Tuning is an important part of building a machine learning model which is aimed at achieving optimal performance of the model. This is a process in which the model is trained to perform and then fine tuning is done to check the parameters to improve its predictability.

• Training of the model: To train an ML model we must select the appropriate data and deal with it before processing it. After handling the missing values, scaling, and splitting data into training and tuning sets we must choose the perfect algorithm we start the training of the model. The model is then trained using the training data, and the algorithm learns the underlying patterns and relationships within the data.
• Tuning of the model: Tuning of the model involves dealing with hyperparameters. Hyperparameters are the configuration settings that are not learned directly from the data during training and they impact the performance of the model. Tuning these models is necessary to improve the performance of the model. Techniques such as grid search, random search, and Bayesian optimization are commonly used for this purpose.

Overview of caret Package

Caret is a powerful package in R which stands for Classification And Regression Training. It is a versatile tool since it provides a wide range of predictive modeling in both classification and regression. This package helps the analyst to experiment without issues of learning multiple algorithms.

To install this package we can use the following command in our R environment.

#installing the package
install.packages("caret")

• Simplified Learning: caret helps in dealing with different machine learning models without knowing each of them in detail.
• Preprocessing Capabilities: We face a lot of errors when we have missing values in our dataset generally when we deal with large data, caret helps in making the data ready by fixing such errors.
• Model Training and Tuning: It helps in creating models that we will learn further in this article.
• Model Evaluation: It also evaluates the model and tells if it needs any correction giving stats of our model.

Machine Learning is an important part of Artificial Intelligence for data analysis. It is widely used in many sectors such as healthcare, E-commerce, Finance, Recommendations, etc. It plays an important role in understanding the trends and patterns in our data to predict useful information that can be used for better decision-making.

There are three types of machine learning:

• Supervised Machine Learning
• Unsupervised Machine Learning
• Reinforcement Machine Learning

In supervised Machine Learning the algorithm learns from the labelled dataset and the goal is to map the input and output from the data to predict future values. In R programming Language It is widely used in various applications such as Regression and Classification to solve real-world issues.

• Regression: This type of model focuses on building relationships between the variables. For example, the prediction of house price based on other variables such as size or location comes under regression.
• Classification: This model involves categorizing data into predefined classes. This type of model is used in email spam detection. Data miners and researchers use ML for predictive analysis and this analysis is made easier by the R programming Language. R is a language that provides a wide range of packages to help in data analysis and predictions. One such package is “caret”. In this article, we will try to understand how to use caret in Supervised Machine Learning by training and tuning ML models with the help of multiple examples.