Contents

- 1 Can we use PCA for feature selection?
- 2 Which feature selection method is best?
- 3 How does PCA reduce features?
- 4 How PCA works in feature selection?
- 5 How is correlation used in feature selection?
- 6 Does PCA improve accuracy?
- 7 When is PCA a valid method of feature selection?
- 8 How are principal components ranked in feature selection?

## Can we use PCA for feature selection?

Principal Component Analysis (PCA) is a popular linear feature extractor used for unsupervised feature selection based on eigenvectors analysis to identify critical original features for principal component. The method generates a new set of variables, called principal components.

**Is PCA better than feature selection?**

The basic difference is that PCA transforms features but feature selection selects features without transforming them. PCA is a dimensionality reduction method but not feature selection method. They all are good for feature selection. Greed algorithm and rankers are also better.

### Which feature selection method is best?

There is no best feature selection method. Just like there is no best set of input variables or best machine learning algorithm. At least not universally. Instead, you must discover what works best for your specific problem using careful systematic experimentation.

**How do you select variables in PCA?**

In each PC (1st to 5th) choose the variable with the highest score (irrespective of its positive or negative sign) as the most important variable. Since PCs are orthogonal in the PCA, selected variables will be completely independent (non-correlated).

## How does PCA reduce features?

Steps involved in PCA:

- Standardize the d-dimensional dataset.
- Construct the co-variance matrix for the same.
- Decompose the co-variance matrix into it’s eigen vector and eigen values.
- Select k eigen vectors that correspond to the k largest eigen values.
- Construct a projection matrix W using top k eigen vectors.

**How do you use PCA algorithm?**

Steps for PCA algorithm

- Getting the dataset.
- Representing data into a structure.
- Standardizing the data.
- Calculating the Covariance of Z.
- Calculating the Eigen Values and Eigen Vectors.
- Sorting the Eigen Vectors.
- Calculating the new features Or Principal Components.
- Remove less or unimportant features from the new dataset.

### How PCA works in feature selection?

The only way PCA is a valid method of feature selection is if the most important variables are the ones that happen to have the most variation in them . Once you’ve completed PCA, you now have uncorrelated variables that are a linear combination of the old variables.

**Does PCA give feature importance?**

PCA technique is particularly useful in processing data where multi-colinearity exists between the features/variables. PCA can be used when the dimensions of the input features are high (e.g. a lot of variables). PCA can be also used for denoising and data compression.

## How is correlation used in feature selection?

How does correlation help in feature selection? Features with high correlation are more linearly dependent and hence have almost the same effect on the dependent variable. So, when two features have high correlation, we can drop one of the two features.

**How do you do cluster selection feature?**

How to do feature selection for clustering and implement it in python?

- Perform k-means on each of the features individually for some k.
- For each cluster measure some clustering performance metric like the Dunn’s index or silhouette.
- Take the feature which gives you the best performance and add it to Sf.

### Does PCA improve accuracy?

Principal Component Analysis (PCA) is very useful to speed up the computation by reducing the dimensionality of the data. Plus, when you have high dimensionality with high correlated variable of one another, the PCA can improve the accuracy of classification model.

**Does PCA reduce Overfitting?**

The main objective of PCA is to simplify your model features into fewer components to help visualize patterns in your data and to help your model run faster. Using PCA also reduces the chance of overfitting your model by eliminating features with high correlation.

## When is PCA a valid method of feature selection?

The only way PCA is a valid method of feature selection is if the most important variables are the ones that happen to have the most variation in them. However this is usually not true. As an example, imagine you want to model the probability that an NFL team makes the playoffs.

**How are variables selected in a PCA analysis?**

In this example I am using the iris data. Before the example, please note that the basic idea when using PCA as a tool for feature selection is to select variables according to the magnitude (from largest to smallest in absolute values) of their coefficients (loadings). See my last paragraph after the plot for more details.

### How are principal components ranked in feature selection?

Those k principal components are ranked by importance through their explained variance, and each variable contributes with varying degree to each component. Using the largest variance criteria would be akin to feature extraction, where principal component are used as new features, instead of the original variables.

**Which is more important PCA or coordinate system?**

PCA is a actually a way of transforming your coordinate system to capture the variation in your data. This does not mean that the data is in any way more important than the other ones. It may be true in some cases while it may have no significance in some.