Lesson 3

Mastering Linear Regression: From Theories to Predictions

Introduction to Linear Regression

Today, we'll delve into the riveting realm of Linear Regression. This core concept forms the backbone of machine learning and predictive modeling. We'll bring our favorite Wine Quality Dataset to spice things up! We aim to untangle the intricacies of Linear Regression, mastering its underlying principles and learning how to implement it using Python's scikit-learn library. We'll use the Wine Quality Dataset to predict the quality of wine.

Probing Linear Regression

Linear Regression is fundamental to supervised learning. It becomes particularly useful when the target or outcome variable is continuous. Let's illustrate this concept with a simple real-world example: suppose you want to predict the price of a house (the output or dependent variable) based on its size (the input or independent variable). In this case, you would use Linear Regression because both your output and input are continuous.

Along the same lines, we will predict the quality of the wine (a numerical score from 0 to 10, which is continuous) based on several physicochemical properties, such as fixed acidity, volatile acidity, and citric acid, using our dataset.

Linear Regression algorithm optimizes a straight line to encapsulate the relationship accurately between the input and output variables. This line is modeled using a simple equation, y=mx+cy = mx + c, where y is the dependent variable, m is the slope, x is the independent variable, and c is the y-intercept.

Mathematics behind Linear Regression

At the heart of Linear Regression lies the concept of the cost function and hypothesis, which we'll break down below:

  1. Hypothesis: This results in the regression line that can predict the output based on the inputs. If we're trying to predict wine quality based on certain properties, this hypothesis would best fit the linear relationship between our selected properties and the wine's quality. The hypothesis is represented as hθ(x)=θ0+θ1xh_\theta(x) = \theta_0 + \theta_1x, where θ0\theta_0 and θ1\theta_1 are the model's parameters.

  2. Cost Function (or Loss Function): This term simply quantifies how wrong our model's predictions are relative to the actual truth. We aim to minimize this function to achieve the most accurate prediction. It's also known as the Mean Squared Error (MSE) and it's given by J(θ)=12mi=1m(hθ(x(i))y(i))2 J(\theta) = \frac{1}{2m} \sum_{i=1}^{m} (h_\theta(x^{(i)}) - y^{(i)})^2 where m is the total count of observations and the summation over the squared differences (errors) ensures that the higher the error, the greater the cost.

These components come together and can be optimized using the Gradient Descent we learned in the previous lesson. Gradient Descent will painstakingly adjust θ0\theta_0 and θ1\theta_1 to minimize the cost function and derive a line that gives us the lowest possible error or cost.

Designing Linear Regression Models

Every well-constructed tower needs a solid design, and building a high-performance regression model is no different! Once the foundation (mathematics) of Linear Regression is established, we leverage Python and its powerful scikit-learn library for the implementation.

You can break down the steps to designing a Linear Regression model as follows:

  1. Start by importing the necessary libraries and classes.
  2. Load the dataset and isolate the features (independent variables) and target variables (dependent variables).
  3. Split the data into training and testing parts: the training set for learning and the testing set for evaluating the model's performance. Here, it's crucial to understand that while splitting the data, the test_size argument represents the proportion of the dataset to include in the test set. The random_state argument ensures reproducibility by controlling the shuffling applied to the data before applying the split.
  4. Create the Linear Regression model using scikit-learn's LinearRegression class.
  5. Finally, assess the model using various performance metrics.

Let's implement this in Python and predict some wine quality:

Python
1from sklearn.model_selection import train_test_split 2from sklearn.linear_model import LinearRegression 3from sklearn import metrics 4import pandas as pd 5 6# Load the wine dataset 7import datasets 8red_wine = datasets.load_dataset('codesignal/wine-quality', split='red') 9red_wine = pd.DataFrame(red_wine) 10 11# Select features and target variable 12features = red_wine.drop('quality', axis=1) 13target = red_wine['quality'] 14 15# Split the dataset into a training set and a testing set 16features_train, features_test, target_train, target_test = train_test_split(features, target, test_size=0.2, random_state=42) 17 18# Instantiate and fit the model 19model = LinearRegression() 20model.fit(features_train, target_train) 21 22# Predict the test features 23predictions = model.predict(features_test) 24 25# Evaluate the model 26mse = metrics.mean_squared_error(target_test, predictions) 27print('Mean Squared Error:', mse) # Mean Squared Error: 0.39002514396395416

To visualize our prediction, let's draw a plot showing the Actual vs Predicted difference:

Python
1import matplotlib.pyplot as plt 2 3# Plot target vs prediction 4plt.scatter(target_test, predictions, color='blue') 5# Plot the ideal prediction line (with zero error) 6plt.plot([target_test.min(), target_test.max()], [target_test.min(), target_test.max()], 'k--', lw=2) 7plt.xlabel('Actual') 8plt.ylabel('Predicted') 9plt.title('Actual vs Predicted') 10plt.show()

image

Examining the Model's Performance

That wasn't too bad, was it? But hold on, we're not done yet. It's crucial to check the model's performance by examining the residuals, simply the difference between the actual and predicted values. The smaller the residuals, the better the model performs. We'll look at two key metrics here:

  1. Mean Squared Error (MSE): The average of the squared errors, with larger errors contributing more due to the squaring. This is the cost function we discussed earlier.
  2. Coefficient of Determination (R-squared): This measures the degree of variation in the target variable that our model could predict. It ranges between 0 and 1, with a higher value representing a higher quality of our model.

Here's how to calculate MSE and R-squared in Python:

Python
1r2_score = metrics.r2_score(target_test, predictions) 2print('R-squared:', r2_score) # R-squared: 0.4031803412796231
Conclusion

We've unraveled the intricacies of Linear Regression, starting from the basic principles, strolling through the supportive mathematical framework, and finally constructing a fully-functioning model with Python and scikit-learn. Now, you should understand the concepts and workings of Linear Regression, its design, implementation, and application for predictive modeling. Based on this newfound knowledge, you've used the Wine Quality Dataset to predict wine quality based on numerous physicochemical features.

Practice Makes Perfect!

Having established the theoretical framework, we're ready to move on to the practical part. Now is the time for some hands-on exercises that will help solidify your knowledge and skills in linear regression models. You'll be equipped to use these models in various situations expertly through these exercises. So, let's delve into the practical exercises!

Enjoy this lesson? Now it's time to practice with Cosmo!

Practice is how you turn knowledge into actual skills.