Evaluate Machine Learning Algorithm in R – dataset split in R

Evaluating the performance of a machine learning algorithm is an important step in understanding how well it will work on new, unseen data. One common method for evaluating the performance of an algorithm is to split the available data into two sets: a training set and a test set.

The training set is used to train the algorithm, while the test set is used to evaluate its performance. The idea is that the algorithm should perform well on the test set if it has learned to generalize from the training set.

In R, there are several ways to split a dataset into training and test sets. One common method is to use the sample() function, which can be used to randomly select a certain proportion of the data for the test set, and the rest for the training set. Another popular package for dataset splitting is the caret package which has a function called createDataPartition that allows you to split the dataset into training and test sets.

One advantage of using dataset split in R is that it’s a simple and easy-to-use method for evaluating the performance of a machine learning algorithm. It can also help you to avoid overfitting, which is when a model is too closely fit to the training data and doesn’t work well on new data.

However, it’s important to keep in mind that the performance of the algorithm is dependent on the split, so you should use a randomization method to ensure that the split is representative of the population. Also, it’s important to use a large enough test set to get an accurate evaluation of the algorithm’s performance.

Overall, dataset split is a simple and effective method for evaluating the performance of a machine learning algorithm in R. It can help you to avoid overfitting and to get an accurate evaluation of the algorithm’s performance. However, it’s important to use a randomization method and a large enough test set to ensure that the results are representative of the population.

In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in R programming: Evaluate Machine Learning Algorithm in R – dataset split in R.

Evaluate Machine Learning Algorithm in R – dataset split in R

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