What does dimensionality reduction reduce?

Related MCQs

• Dimensionality Reduction Algorithms are one of the possible ways to reduce the computation time required to build a model
• Dimensionality reduction algorithms are one of the possible ways to reduce the computation time required to build a model.
• It is not necessary to have a target variable for applying dimensionality reduction algorithms
• The most popularly used dimensionality reduction algorithm is Principal Component Analysis (PCA). Which of the following is/are true about PCA? 1. PCA is an unsupervised method2. It searches for the directions that data have the largest variance3. Maximum number of principal components <= number of features4. All principal components are orthogonal to each other
• The "curse of dimensionality" referes
• Which of the following can help to reduce overfitting in an SVM classifier?
• Suppose your model is demonstrating high variance across the different training sets. Which of the following is NOT valid way to try and reduce the variance?
• Having built a decision tree, we are using reduced error pruning to reduce the size of the tree. We select a node to collapse. For this particular node, on the left branch, there are 3 training data points with the following outputs: 5, 7, 9.6 and for the right branch, there are four training data points with the following outputs: 8.7, 9.8, 10.5, 11. What were the original responses for data points along the two branches (left & right respectively) and what is the new response after collapsing the node?
• What are the steps for using a gradient descent algorithm? 1)Calculate error between the actual value and the predicted value 2)Reiterate until you find the best weights of network 3)Pass an input through the network and get values from output layer 4)Initialize random weight and bias 5)Go to each neurons which contributes to the error and change its respective values to reduce the error
• Which of the following is true about bagging? 1. Bagging can be parallel 2. The aim of bagging is to reduce bias not variance 3. Bagging helps in reducing overfitting