How can I select the most informative features from a big feature set?

How can I select the most informative features from a big feature set?

You can use Principle Component Analysis to select most informative features from a big feature set. High dimensional reduction is one of the important step for Data analysis.

How do you reduce the size of data?

3. Common Dimensionality Reduction Techniques

1. 3.1 Missing Value Ratio. Suppose you’re given a dataset.
2. 3.2 Low Variance Filter.
3. 3.3 High Correlation filter.
4. 3.4 Random Forest.
5. 3.5 Backward Feature Elimination.
6. 3.6 Forward Feature Selection.
7. 3.7 Factor Analysis.
8. 3.8 Principal Component Analysis (PCA)

Which of the following is more appropriate to do feature selection?

For feature selection, we would prefer to use lasso since solving the optimization problem when using lasso will cause some of the coefficients to be exactly zero (depending of course on the data) whereas with ridge regression, the magnitude of the coefficients will be reduced, but won’t go down to zero.

What is tree based feature selection?

2.1. Decision tree. In the feature selection method based on decision tree, the process of constructing decision tree is the process of feature selection. In such way, it can reduce the size of the decision tree and avoid the problem of inaccurate decision trees.

How is feature importance calculated?

Feature importance is calculated as the decrease in node impurity weighted by the probability of reaching that node. The node probability can be calculated by the number of samples that reach the node, divided by the total number of samples. The higher the value the more important the feature.

How do you find a feature important?

Probably the easiest way to examine feature importances is by examining the model’s coefficients. For example, both linear and logistic regression boils down to an equation in which coefficients (importances) are assigned to each input value.