The bias-variance trade-off is a fundamental concept in machine learning that refers to the relationship between a model's ability to fit the training data and its ability to generalize to new, unseen data.
Bias refers to the difference between the expected or average prediction of a model and the true value of the target variable. High bias means that the model is too simple and fails to capture the underlying patterns in the data, leading to underfitting.
Unlock the full potential of big data with our comprehensive Data Science Course Video. Gain hands-on experience with the latest tools and techniques in data analysis, machine learning and data visualization. Our expert instructors will guide you every step of the way to ensure you have the skills and confidence to tackle real-world data science projects. Watch now and take your career to the next level with the most in-demand skills of the 21st century.
Variance, on the other hand, refers to the amount by which the model's predictions vary for different training datasets. High variance means that the model is too complex and overfits the training data, capturing the noise and randomness in the data, and performing poorly on new data.
The trade-off between bias and variance occurs because reducing one typically increases the other. Finding an optimal balance between bias and variance is crucial to building a model that performs well on both the training and testing data. This can be achieved through various techniques such as regularization, cross-validation, and ensembling.