Can correlation be used for feature selection?

Can correlation be used for 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 I apply feature selection in Weka?

A good place to get started exploring feature selection in Weka is in the Weka Explorer.

1. Open the Weka GUI Chooser.
2. Click the “Explorer” button to launch the Explorer.
3. Open the Pima Indians dataset.
4. Click the “Select attributes” tab to access the feature selection methods.

What is correlation attribute eval in Weka?

Class CorrelationAttributeEval

Evaluates the worth of an attribute by measuring the correlation (Pearson’s) between it and the class. Nominal attributes are considered on a value by value basis by treating each value as an indicator. An overall correlation for a nominal attribute is arrived at via a weighted average.

What is CfsSubsetEval Weka?

CfsSubsetEval : Evaluates the worth of a subset of attributes by considering the individual predictive ability of each feature along with the degree of redundancy between them. Subsets of features that are highly correlated with the class while having low intercorrelation are preferred.

How do you use Pearson correlation for feature selection?

Feature Selection-How To Drop Features Using Pearson Correlation

How do you drop a feature based on correlation?

How to drop out highly correlated features in Python?

1. Recipe Objective.
2. Step 1 – Import the library.
3. Step 2 – Setup the Data.
4. Step 3 – Creating the Correlation matrix and Selecting the Upper trigular matrix.
5. Step 5 – Droping the column with high correlation.
6. Step 6 – Analysing the output.

What is correlation based feature selection?

CFS (Correlation based Feature Selection) is an algorithm that couples this evaluation formula with an appropriate correlation measure and a heuristic search strategy. CFS was evaluated by experiments on artificial and natural datasets.

What is correlation attribute eval?

CorrelationAttributeEval : Evaluates the worth of an attribute by measuring the correlation (Pearson’s) between it and the class. Nominal attributes are considered on a value by value basis by treating each value as an indicator. An overall correlation for a nominal attribute is arrived at via a weighted average.

What is attribute selection filter in Weka?

A supervised attribute filter that can be used to select attributes. It is very flexible and allows various search and evaluation methods to be combined.

What is WrapperSubsetEval?

WrapperSubsetEval: Evaluates attribute sets by using a learning scheme. Cross validation is used to estimate the accuracy of the learning scheme for a set of attributes.

How can I check the correlation between features and target variable?

You can evaluate the relationship between each feature and target using a correlation and selecting those features that have the strongest relationship with the target variable. The difference has to do with whether features are selected based on the target variable or not.

How do you identify highly correlated features?

The quickest and often the best method of identifying highly correlated features is to use a correlation matrix. This matrix shows the correlation between every single pair of numeric features in the dataset.

Should we remove correlated features?

In general, it is recommended to avoid having correlated features in your dataset. Indeed, a group of highly correlated features will not bring additional information (or just very few), but will increase the complexity of the algorithm, thus increasing the risk of errors.

How much correlation is too much?

Correlation coefficients whose magnitude are between 0.9 and 1.0 indicate variables which can be considered very highly correlated. Correlation coefficients whose magnitude are between 0.7 and 0.9 indicate variables which can be considered highly correlated.

How do you find the correlation between features?

What is InfoGainAttributeEval in Weka?

InfoGainAttributeEval : Evaluates the worth of an attribute by measuring the information gain with respect to the class. InfoGain(Class,Attribute) = H(Class) – H(Class | Attribute).

How can we increase Weka accuracy?

Feature Selection to Improve Accuracy and Decrease Training Time

1. Carefully choose features in your dataset.
2. Feature Selection Methods in the Weka Explorer.
3. Creating Transforms of a Dataset using Feature Selection methods in Weka.
4. Coupling a Classifier and Attribute Selection in a Meta Algorithm in Weka.

How will you choose one features if there are 2 highly correlated features?

When we have highly correlated features in the dataset, the values in “S” matrix will be small. So inverse square of “S” matrix (S^-2 in the above equation) will be large which makes the variance of Wₗₛ large. So, it is advised that we keep only one feature in the dataset if two features are highly correlated.

How do you handle correlated features?

First, I will use a greedy algorithm to eliminate features with respect to their correlation to other features.
…
4 Feature Reduction

1. 4.1 Greedy Elimination.
2. 4.2 Recursive Feature Elimination (RFE)
3. 4.3 Lasso Regularision.
4. 4.4 Principle Component Analysis (PCA)

What is a good value for correlation?

In summary: As a rule of thumb, a correlation greater than 0.75 is considered to be a “strong” correlation between two variables. However, this rule of thumb can vary from field to field. For example, a much lower correlation could be considered strong in a medical field compared to a technology field.

How do you know if a correlation is strong?

The relationship between two variables is generally considered strong when their r value is larger than 0.7. The correlation r measures the strength of the linear relationship between two quantitative variables.

What are 3 examples of correlation?

Positive Correlation Examples

• Example 1: Height vs. Weight.
• Example 2: Temperature vs. Ice Cream Sales.
• Example 1: Coffee Consumption vs. Intelligence.
• Example 2: Shoe Size vs. Movies Watched.

What is TP rate and FP rate in Weka?

TP Rate: rate of true positives (instances correctly classified as a given class) FP Rate: rate of false positives (instances falsely classified as a given class) Precision: proportion of instances that are truly of a class divided by the total instances classified as that class.

Does feature selection improve classification accuracy?

The main benefit claimed for feature selection, which is the main focus in this manuscript, is that it increases classification accuracy. It is believed that removing non-informative signal can reduce noise, and can increase the contrast between labelled groups.

How do you deal with highly correlated features?

The easiest way is to delete or eliminate one of the perfectly correlated features. Another way is to use a dimension reduction algorithm such as Principle Component Analysis (PCA).