Applied Data Science Coding in Python: How to prepare train test dataset

Applied Data Science Coding in Python: How to prepare train test dataset

Preparing a train and test dataset is an important step in the machine learning process. The train dataset is used to train the model, while the test dataset is used to evaluate the performance of the model. The goal is to train the model on one dataset and evaluate it on a separate and independent dataset.

One way to prepare a train and test dataset in Python is to use the train_test_split function from the scikit-learn library. This function allows you to easily split your data into a training and testing set. You can specify the proportion of the data that you want to use for training and testing. For example, you can choose to use 80% of the data for training and 20% for testing.

Another way to prepare a train and test dataset is by using the pandas.DataFrame.sample method. This method allows you to randomly sample the dataframe and split it into the train and test datasets.

It is important to keep in mind that when preparing the train and test dataset you should ensure that the data is randomly sampled, so that the model will not be trained on some specific pattern found in the data. It’s also important to ensure that the distribution of the target variable is similar in the train and test datasets.

It’s also a good practice to use stratified sampling when the target variable is categorical, this will ensure that the distribution of the target variable is similar in the train and test dataset.

In summary, preparing a train and test dataset is an important step in the machine learning process. It allows you to train the model on one dataset and evaluate it on a separate and independent dataset. There are several ways to prepare train and test datasets in Python, one popular method is using the train_test_split function from the scikit-learn library, or using the pandas.DataFrame.sample method. It is important to ensure that the data is randomly sampled and that the distribution of the target variable is similar in the train and test datasets.

 

In this Applied Machine Learning & Data Science Recipe, the reader will learn: How to prepare train test dataset.



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