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ML (Can you explain the bias-variance trade-off in machine learning?) -…
ML
Can you explain the bias-variance trade-off in machine learning?
Bias
(Underfitting)
overly simplistic assumptions
Variance
(Overfitting)
sensitivity to small fluctuations or noise in the training data
as you reduce bias, you tend to increase variance
Solutions
Regularization techniques
(L1 and L2 regularization)
a set of methods to prevent overfitting and improve the generalization performance of models
add constraints or penalties to the model's learning process, encouraging it to be simpler and less prone to overfitting
L1 Regularization
(Lasso Regularization)
a penalty is added to the model's cost function that is proportional to the absolute values of its coefficients
encourages some of the model's coefficients to become exactly zero
useful for feature selection because it tends to set the weights of less important features to zero
can help create sparse models, which are models that use only a subset of the available features.
L2 Regularization
(Ridge Regularization)
a penalty is added to the model's cost function that is proportional to the square of its coefficients
discourages large coefficient values and encourages all features to have small, non-zero weights
helps prevent the model from becoming overly sensitive to the specific training data and reduces the risk of overfitting
often used when you have many features, and you want to prevent any single feature from dominating the model
Cross-validation
used to assess the performance and generalization of a predictive model
particularly helpful in estimating how well a model will perform on unseen data
Data Splitting
The first step is to divide the available dataset into two or more subsets: a training set and a testing set.
testing set
evaluating the performance
training set
to train the machine learning model
K-Fold Cross-Validation
the training data is further divided into K equally sized "folds" or subsets
the model is then trained and evaluated K times, each time using a different fold as the testing set and the remaining folds as the training set
the results of each iteration are averaged to obtain a final performance metric
Performance Evaluation
during each iteration of cross-validation, the model is trained on one subset (fold) and tested on another
The evaluation metric(s) of interest, such as accuracy, mean squared error, or F1 score, is recorded for each iteration.
Averaging Results
After K iterations, the performance metrics are averaged to obtain a more robust and reliable estimate of the model's performance.
Feature engineering
Underfitting
(too simple and high bias)
a model learns to fit the training data too closely, including noise and irrelevant details, which can lead to poor performance on new, unseen data
Overfitting
(too complex and has high variance)
A model with high variance is highly flexible and can fit the training data very closely, but it may fail to generalize well to new, unseen data because it essentially memorizes the noise in the training data rather than learning the true underlying patterns