Machine Learning MCQ - How to prevent overfitting in decision trees

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Machine Learning MCQ - Prevent or reduce overfitting in decision trees - How?

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1. Select all strategies below that can help prevent or reduce overfitting in decision trees

a) Not restricting the depth of the decision tree

b) Pruning the decision tree based on a validation set accuracy

c) Use more features to represent each example

d) None of the above

 

Answer: (b) Pruning the decision tree based on a validation set accuracy   Decision tree pruning is one of many techniques used to prevent the tree from overfitting.   Pruning is a technique that removes parts of the decision tree and prevents it from growing to its full depth and complex. Pruning removes those parts of the decision tree that do not have the power to classify instances. A validation set is a subset of data (or training data) used to evaluate and improve a model's performance during training. We say that a machine learning model overfits when it shows low training error and high true error. Overfitting occurs when a model fits too closely to the training data and may become less accurate when encountering new data or predicting future outcomes. If the training error is much lower than the validation error, it means that the model is overfitting the training data.   Why not option (a)? Letting a tree to grow beyond a depth might lead to overfit. To limit the growth of a decision tree, maximum depth can be set. Maximum depth a decision tree is allowed to grow is a type of pruning techniques (pre-pruning).   Why not option (c)? Selection of the most relevant and informative features to use in the Decision Tree is very much necessary rather than using more features.   How to avoid overfitting in decision trees? We can use one or more of the following to overcome overfitting in decision trees; Pruning Regularization Early stopping Hyperparameter tuning Ensemble methods Minimum samples per leaf node Cross-validation Training with more data Data augmentation

 

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What is pruning in decision trees in machine learning?

Common problem in decision tree is overfitting

What is validation set and how does its accuracy helps in pruning the decision trees?

Overfit means low training error and high test (true) error. It is considered as the failure of the model to generalize

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Machine Learning MCQ - Learning dataset with 0 entropy are not good for learning

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Machine Learning MCQ - Zero entropy training examples are not good for learning in classification task

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1. In a classification problem, if the entropy of a set of training examples is zero, then the training examples are

a) good for learning

b) not good for learning

c) good for learning during training but not for testing

d) good for learning during testing but not for training

Answer: (b) not good for learning Entropy is a measure of uncertainty or randomness in a dataset. When entropy becomes 0, then the dataset has no impurity. Datasets with 0 impurities are not useful for learning because each example from the training set belong to only one class.   What is zero entropy? For each training example, the model predicts a single class with 100% certainty (probability = 1 for one class, and 0 for all others). This implies that the model is perfectly confident about the class of every training example.   A dataset with low entropy is more predictable and easier to classify. The higher the entropy, the harder it is to draw any conclusions from that information.   Low entropy means less uncertain and high entropy means more uncertain.

 

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What is entropy in machine learning?

Why entropy is important in classification machine learning?

Why zero entropy training data are not good for learning purpose?

High entropy is good for training whereas low entropy is not good for training

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Machine Learning MCQ - Which of the following is true about dropout in a neural network

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Machine Learning MCQ - Application of dropout in a neural network to reduce overfitting

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1. Which of the following is true about dropout?

a) Dropout leads to sparsity in the trained weights

b) At test time, dropout is applied with inverted keep probability

c) The larger the keep probability of a layer, the stronger the regularization of the weights in that layer

d) Dropout is applied to different layers of a neural network, but not the output layer

 

Answer: (d) Dropout is applied to different layers of a neural network, but not the output layer Dropout is a machine learning technique that randomly disables a portion of neurons in a neural network during training to prevent overfitting. It works by randomly "dropping out" (setting to zero) a fraction of the neurons (units) in a layer during each forward pass in training. This forces the network to become more robust by preventing it from relying too heavily on any one neuron, thus encouraging the network to learn more diverse features. Dropout can be applied on input layer (to remove deemed to be irrelevant data), and hidden layers (because much of the intermediate processing would end up noise) of a neural network but not on the output layer.   What is dropout?  The term “dropout” refers to dropping out the nodes (input and hidden layer) in a neural network. All the forward and backwards connections with a dropped node are temporarily removed, thus creating new network architecture out of the parent network. The nodes are dropped by a dropout probability of p.   Why dropout is not used in output layer? Dropout is typically not used in the output layer of a neural network because the output layer is responsible for making final predictions, and this layer should produce deterministic and stable results. Random dropout could interfere with the reliability of those predictions.  Alternate to dropout at the output layer? If needed one could use any other regularization techniques that do not affect the stability of the prediction at the output layer. Why not option (b)? Keep probability is the probability of retaining neurons during dropout. Also, dropout is applied during training but not during testing phase.  Why not option (c)? Having a larger keep probability (say 95% of neurons are kept during dropout) may lead to overfit problem. In such cases, dropout may not be effective.

 

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What is dropout in a neural network and why is it used?

Where can we use dropout in a neural network?

Why we cannot use dropout technique in the output layer of a neural net?

If at all you need to use some technique to overcome overfitting in the output layer, what we can do?

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