# Dimensionality Reduction

Welcome to your quiz on Dimensionality Reduction.

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Q1. Imagine, you have 1000 input features and 1 target feature in a machine learning problem. You have to select 100 most important features based on the relationship between input features and the target features.

Do you think, this is an example of dimensionality reduction?

Q2. I have 4 variables in the dataset such as – A, B, C & D. I have performed the following actions:

Step 1: Using the above variables, I have created two more variables, namely E = A + 3 * B and F = B + 5 * C + D.

Step 2: Then using only the variables E and F I have built a Random Forest model.

Could the steps performed above represent a dimensionality reduction method?

Q3. Which of the following techniques would perform better for reducing dimensions of a data set?
Q4. Dimensionality reduction algorithms are one of the possible ways to reduce the computation time required to build a model?
Q5. Which of the following algorithms cannot be used for reducing the dimensionality of data?
Q6. 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 method

2. It searches for the directions that data have the largest variance

3. Maximum number of principal components <= number of features

4. All principal components are orthogonal to each other

Q7. Suppose we are using dimensionality reduction as pre-processing technique, i.e, instead of using all the features, we reduce the data to k dimensions with PCA. And then use these PCA projections as our features. Which of the following statement is correct?

Q8. In which of the following scenarios is t-SNE better to use than PCA for dimensionality reduction while working on a local machine with minimal computational power?
Q9. Which of the following statement is true for a t-SNE cost function?
Q10. Imagine you are dealing with text data. To represent the words you are using word embedding (Word2vec). In word embedding, you will end up with 1000 dimensions. Now, you want to reduce the dimensionality of this high dimensional data such that, similar words should have a similar meaning in nearest neighbor space.In such case, which of the following algorithm are you most likely choose?
Q11. Which of the following statement is correct for t-SNE and PCA?
Q12. What will happen when eigenvalues are roughly equal?
Q13. PCA works better if there is?

1. A linear structure in the data

2. If the data lies on a curved surface and not on a flat surface

3. If variables are scaled in the same unit

Q14. What happens when you get features in lower dimensions using PCA?

1. The features will still have interpretability

2. The features will lose interpretability

3. The features must carry all information present in data

4. The features may not carry all information present in data

Q15. Under which condition SVD and PCA produce the same projection result?

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