Top 3 Machine Learning Quiz Questions with Answers explanation, Interview questions on machine learning, quiz questions for data scientist answers explained, machine learning exam questions, question bank in machine learning, overfitting in non-parametric machine learning algorithms, decision tree, lasso regression
Machine learning Quiz Questions - Set 26
1. What strategies can help reduce over-fitting in decision trees?
a) Pruning
b) Make sure each leaf node is one pure class
c) Enforce a maximum depth for the tree
d) Enforce a maximum number of samples in leaf nodes
Answer: (a) Pruning and (c) Enforce a maximum depth for the tree Over-fitting is a
significant practical difficulty for decision tree models and many other predictive
models. Over-fitting happens when the learning algorithm continues to develop
hypotheses that reduce training set error at the cost of an Unlike other regression models, decision tree doesn’t use regularization to fight against over-fitting. Instead, it employs tree pruning. Selecting the right hyper-parameters (tree depth and leaf size) also requires experimentation, e.g. doing cross-validation with a hyper-parameter matrix. For more information, please refer here. |
2. Neural networks
a) cannot be used in ensemble
b) can be used for regression
c) can be used for classification
d) always output values will be between 0 and 1
Answer: (b) can be used for regression and (c) can be used for classification Regression refers to predictive modeling problems that involve predicting a numeric value given an input. Classification refers to predictive modeling problems that involve predicting a class label or probability of class labels for a given input. Neural networks can be used for either regression or classification. Under regression model a single value is outputted which may be mapped to a set of real numbers meaning that only one output neuron is required. Under classification model an output neuron is required for each potentially class to which the pattern may belong. If the classes are unknown unsupervised neural network techniques such as self organizing maps should be used. For more information, please refer here. |
3. Lasso can be interpreted as least-squares linear regression where
a) weights are regularized with the l_{1} norm
b) the weights have a Gaussian prior
c) weights are regularized with the l_{2} norm
d) the solution algorithm is simpler
Answer: (a) weights are regularized with the l_{1} norm
Regularization is a technique to deal with over-fitting problem. Lasso regression Lasso regression is a regularization technique. This model uses shrinkage. Shrinkage is where data values are shrunk towards a central point as the mean. The lasso procedure encourages simple, sparse models (i.e. models with fewer parameters). A sparse solution could avoid over-fitting. Lasso regression performs L1 regularization, which adds a penalty equal to the absolute value of the magnitude of coefficients. This type of regularization can result in sparse models with few coefficients; Some coefficients can become zero and eliminated from the model. Why l_{1} norm? By L1 regularization, you essentially make the vector smaller (sparse), as most of its components are useless (zeros), and at the same time, the remaining non-zero components are very “useful”. |
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