Adventures in Machine Learning

Splitting DataFrames for Machine Learning: Train_Test or Sample?

Are you looking to split your DataFrame into train and test sets for machine learning modeling? It’s a crucial step in the process, and thankfully there are a few ways to do it.

In this article, we’ll explore two popular methods: using train_test_split() from sklearn and sample() from pandas. Method 1: Using train_test_split() from sklearn

The first method we’ll explore is using train_test_split() from the Python library sklearn.

This method randomly splits the DataFrame into a training set and a testing set, with a designated proportion allocated for each. Using this method is simple – make sure you have sklearn installed, then import the method and use it to split the DataFrame:

“`python

from sklearn.model_selection import train_test_split

# Splitting a DataFrame using train_test_split()

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

“`

Here, `X` and `y` are the features and target variable, respectively, and the `test_size` parameter determines the proportion of the DataFrame to use for testing (in this case, 20%).

The `random_state` parameter is used to set a seed value for randomization, ensuring reproducibility. Method 2: Using sample() from pandas

The second method we’ll explore uses the sample() method from the pandas library to randomly select rows from the DataFrame for the training and testing sets.

“`python

# Splitting a DataFrame using sample()

train = df.sample(frac=0.8, random_state=42)

test = df.drop(train.index)

“`

Here, `df` is the DataFrame to split, and `frac` specifies the percentage of rows to include in the training set (in this case, 80%). The `random_state` parameter again sets a seed value for reproducibility.

Using train_test_split() to Split a DataFrame

Now that we’ve explored the two methods to split a DataFrame, let’s dive in deeper into using train_test_split() to split a DataFrame. We’ll start by creating our own sample DataFrame for illustrative purposes.

“`python

# Creating a sample DataFrame

df = pd.DataFrame({

‘Name’: [‘Bob’, ‘Alice’, ‘Charlie’, ‘David’, ‘Emily’],

‘Age’: [32, 24, 45, 18, 27],

‘Gender’: [‘M’, ‘F’, ‘M’, ‘M’, ‘F’],

‘Income’: [45000, 25000, 80000, 15000, 35000],

‘Score’: [78, 92, 81, 65, 87]

})

“`

Our sample DataFrame has five rows and five columns, representing information about five individuals. We’ll now use train_test_split() to split the DataFrame.

“`python

# Splitting the DataFrame using train_test_split()

X_train, X_test, y_train, y_test = train_test_split(df[[‘Age’, ‘Income’, ‘Score’]], df[‘Gender’], test_size=0.3, random_state=42)

“`

Here, we’re splitting the DataFrame into features (Age, Income, Score) and target (Gender), with a 70-30 split for the training and testing sets, respectively.

Conclusion

Splitting a DataFrame into train and test sets is an essential step when building machine learning models. We explored two popular methods – using train_test_split() from sklearn and sample() from pandas – and learned how to use train_test_split() on a sample DataFrame.

Each method has its advantages and disadvantages, so choose the one that best suits your needs. Remember to set a seed value for reproducibility, and happy modeling!

3) Using sample() to Split a DataFrame

Another way to split a DataFrame into train and test sets is by using sample() method from the Pandas library. The sample() method is used to randomly select rows or columns from a DataFrame.

We can use sample() to randomly select a certain percentage of rows for the training set and the remaining rows for the testing set. Let’s look at an example to see how we can use sample() to split a DataFrame.

“`python

# Creating a sample DataFrame

df = pd.DataFrame({

‘Name’: [‘Bob’, ‘Alice’, ‘Charlie’, ‘David’, ‘Emily’],

‘Age’: [32, 24, 45, 18, 27],

‘Gender’: [‘M’, ‘F’, ‘M’, ‘M’, ‘F’],

‘Income’: [45000, 25000, 80000, 15000, 35000],

‘Score’: [78, 92, 81, 65, 87]

})

# Splitting the DataFrame using sample()

train_df = df.sample(frac=0.7, random_state=42)

test_df = df.drop(train_df.index)

“`

In this example, we first create a sample DataFrame with information about five individuals. We then use sample() to randomly select 70% of the rows for the training set and the remaining rows for the testing set.

We use random_state=42 to ensure that we get the same split every time we run the code. It is important to note that when using sample(), the random selection of rows may not provide an even representation of the data.

There may be some categories or classes that are overrepresented or underrepresented in the training or testing set. Therefore, it’s important to use this method with caution and ensure that the resulting data split is representative of the entire dataset.

4)

Conclusion

In conclusion, splitting a DataFrame into train and test sets is an essential step in building machine learning models. We explored two popular methods to split a DataFrame: using train_test_split() from the sklearn library and using sample() from the Pandas library.

Both methods are effective, but the choice between them may depend on the specific use case and data structure.

train_test_split() is a convenient method that allows a direct split between the features and the target variables.

It also incorporates a randomization technique that ensures that the data is shuffled before it is divided. On the other hand, sample() allows for more flexible splits and enables you to randomize based on row and column percentages.

However, it may not always provide an even representation of the data in the splits. It’s essential to choose the right method for your specific needs and data structure.

Regardless of the method, always set a seed value to ensure reproducibility. We hope this article has been informative and useful in your machine learning endeavors!

In conclusion, splitting a DataFrame into train and test sets is a crucial step when building machine learning models.

We explored two popular methods for splitting a DataFrame, namely using train_test_split() from the sklearn library and using sample() from the Pandas library. While both methods have their advantages and limitations, it’s crucial to choose the one that best suits your data structure and needs.

Always set a seed value for reproducibility, and ensure that the resulting splits are representative of the entire dataset. With these considerations in mind, you’ll be well on your way to building accurate and robust machine learning models.

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