The best fit line in Machine learning represents the optimum model that fits perfectly with the input and output variables. In this case, the output variables are mostly continuous values. We know that the machine learning model is a model that actually finds the relation between the input variables and the output variables and understands those relations. And whenever we provide only the input value, based on its understanding, it predicts the output variable for us. This understanding process is actually known as the fitting of the model. The best fit in machine learning represents the optimum learning of the model.
What is the Best Fit line in Machine Learning?
We will try to understand the best fit line with the help of one of the popular machine learning algorithms known as the linear regression algorithm. The linear regression algorithm takes the input values, and finds the best-fitted line in relation to the output values.
The best-fit line is often represented by the linear equation.
y = mx + b
- y: The y represents the output value
- m: The m represents the slope of the line
- x: The x represents the input variable
- b: Here the b represents the constant variable
The model trains on the training dataset in order to find the optimum values for m and b so that it can find the best-fitted line which will be used for the predictions later.
Visualize the best-fit line in Machine Learning
Depending on your purpose, you can visualize the best-fit model or line in machine learning. Here we will take an example of linear regression and visualize its best-fitted line.
Let us say that we have the dataset which is shown below and we want to find the best fit line in machine learning.
Now, we trained the linear regression model on this dataset ( learn how we trained the model from the Linear regression post) and we will visualize the trained model with the predicted values.
# plotting the actual values in dotted form plt.scatter(X_test, y_test, color='red') # blue line for the best fitted line plt.scatter(X_test, y_pred, color='blue') # defining the title plt.title('Open vs Closing price') # showing the graph plt.show()
As you can see, the blue line shows the best-fitted line. If you fail to obtain such a line, then you need to apply various kinds of hyperparameter tuning methods in order to find the optimum model.
In this short article, we discussed what is the best-fit line and how to visualize it in simple ways. If you are struggling to get the best-fitted line, that means you need to tune your model or find the optimum values for the parameters so that you can find the best-fitted line.