Which of the following classification techniques may suffer from the overfitting issue? Choose all that apply. A. Decision Tree B. Logistic Regression c. Neural Network d. Naive Bayes |
Actually Overfitting is very common for any machine learning model...
overfitting in the sense the machine learning model have high variance and low bias then the model is called overfitting.
by develop the tree overfitting occurs.by removing over fitting in decision tree we used pre-pruning or post-pruning techniques
logistic regression also faced overfitting model..MNIST data have many sample when we train the data and later test the data we can observe the overfitting.it have high variance
neural network also have overfitting when the training set error is small.when new data present in the neural network by applying forward pass and back propagation error is too large this make overfit the neural network...
naive bayes also sometimes faced overfitting when train is not properly.naive bayes suffer with underfitting in the sense high bias..we used laplase smoothing to avoid this...
all are faced overfitting
#if you have any doubts comment below...
Which of the following classification techniques may suffer from the overfitting issue? Choose all that apply....
Which of the following techniques does NOT prevent a model from overfitting? A) Dropout B) Early stopping C) Data augmentation D) None of the above
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(d) Bayesian learning: Which of the following statements are true for BOTH decision trees and Naive Bayes classifiers (you may choose more than one statement)? (a) All features are assumed to be independent (b) No features are assumed to be independent (c) All features are assumed to be independent given the class label (d) No features are assumed to be independent given the class label Give a brief explanation:
Implement the following machine learning tasks, utilizing Regression techniques e Prediction e Classification eFeature Reduction Feature Independence Model Selection (underfitting and overfitting analysis) 2 Required Components You may utilize Python or Matlab libraries, for the following implementations. 1. Implement prediction utilizing multiple linear regression on a data set with several features Perform an evaluation of the residuals to check for assumptions of your model, such as li earity, noise term with zero mean and constant variance, normality and so forth....
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