Splitting Data into Train and Test Sets

Hello and welcome! In today's lesson, we will explore the critical step of splitting data into training and testing sets, which is foundational for building any robust regression model. By the end of this lesson, you'll be capable of taking a dataset and accurately dividing it into training and testing sets.

When developing a machine learning model, it's essential to test its performance on unseen data. This is because while a model may perform well on the training set, it is the performance on the testing set that determines how well the model generalizes to new, unseen data. Without this split, we risk overestimating the model's capabilities due to its exposure to the training data alone.

By splitting the dataset into training and testing sets, we allow the model to learn on one subset of the data (training set) and evaluate its performance on another subset (testing set). This ensures that the model generalizes well to new data, making it more robust and reliable.

Before we can use the diamonds dataset in our data sets, we need to preprocess it by converting categorical variables into numerical values.

One-hot encoding is a method where each category value is converted into a new binary column. Each column represents a category, and the values are 0 or 1, indicating the absence or presence of the category. This is particularly useful for machine learning algorithms that require numerical input and can benefit from each category being represented as a distinct feature. In Pandas, we use the pd.get_dummies function to achieve one-hot encoding.

Here's how to implement one-hot encoding for our diamonds dataset:

import seaborn as sns
import pandas as pd

# Load the diamonds dataset
diamonds = sns.load_dataset('diamonds')

# Convert categorical variables to dummy/indicator variables
diamonds = pd.get_dummies(diamonds, drop_first=True)

# Display first few rows of the transformed dataset
print(diamonds.head())

Output:

   carat  depth  table  price     x     y     z  cut_Premium  cut_Very Good    cut_Good  cut_Fair   color_E  ...
0   0.23   61.5   55.0    326  3.95  3.98  2.43        False          False       False     False      True  ...
1   0.21   59.8   61.0    326  3.89  3.84  2.31         True          False       False     False      True  ...
2   0.23   56.9   65.0    327  4.05  4.07  2.31        False          False        True     False      True  ...
3   0.29   62.4   58.0    334  4.20  4.23  2.63         True          False       False     False     False  ...
4   0.31   63.3   58.0    335  4.34  4.35  2.75        False          False        True     False     False  ...

The drop_first=True parameter in the pd.get_dummies function drops the first category of each categorical variable to avoid multicollinearity, allowing the remaining categories to uniquely represent the data. This ensures that each feature set used in the model is statistically independent and prevents redundancy. Consequently, models can be trained more efficiently without the risk of perfect collinearity.

Note that one-hot encoding is similar but different to the previously mentioned method of categorical encoding, which converts each category to a unique integer code. Categorical encoding is less memory-intensive and can be useful for models that can handle categorical features natively, such as tree-based models. However, for linear models and certain other algorithms, one-hot encoding is often preferred as it avoids the implicit ordinal relationship inferred by categorical encoding.