What is bias and variance in a machine learning project?
I am currently engaged as a machine learning engineer and I am currently working on a particular project in which I need to develop a model that can predict housing prices based on various features such as location, size, etc. Explain the concept of bias and variance for me so that I can influence positively my model.
In the context of data science, bias and variance are the two key concepts in the context of machine learning that can affect the performance of models:-
Bias
It refers to the error which is introduced by approximating a real-world problem with a simplified model. If a model has a high bias score then it means that there is a robust assumptions about the underlying data.
Variance
It refers to the sensitivity of a model to small fluctuations and even smaller noise in the training data. If a model has a high rate of variance then it means that it is overly complex and captured noises in the training data.
Here is a Python script given which demonstrates how you can train a model and even evaluate its bias or variance by using the method of cross-validation:-
From sklearn.model_selection import cross_val_score
From sklearn.linear_model import LinearRegression
# Sample training data (replace with actual data)
X_train = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
Y_train = [10, 20, 30]
# Initialize linear regression model
Model = LinearRegression()
# Evaluate model performance using cross-validation
Cv_scores = cross_val_score(model, X_train, y_train, cv=5)
# Calculate bias and variance
Bias = 1 – cv_scores.mean()
Variance = cv_scores.std()
Print(“Bias:”, bias)
Print(“Variance:”, variance)