How do you select features for a model What do you look for?
Feature Selection: Select a subset of input features from the dataset.
- Unsupervised: Do not use the target variable (e.g. remove redundant variables). Correlation.
- Supervised: Use the target variable (e.g. remove irrelevant variables). Wrapper: Search for well-performing subsets of features. RFE.
What are the feature selection techniques?
It can be used for feature selection by evaluating the Information gain of each variable in the context of the target variable.
- Chi-square Test.
- Fisher’s Score.
- Correlation Coefficient.
- Dispersion ratio.
- Backward Feature Elimination.
- Recursive Feature Elimination.
- Random Forest Importance.
How to choose the best minPts for DBSCAN?
The first thing to do for DBSCAN is to find a good distance function for your application. Do not rely on Euclidean distance being the best for every application! minPts is selected based on the domain knowledge.
How to find the optimal Epsilon value for DBSCAN?
One method used to estimate the optimal epsilon value is to use nearest neighbor distances. If you recall, nearest neighbors is a supervised ML clustering algorithm which clusters new data points based on their distance from other “known” data points.
When are data points valid neighbors in DBSCAN?
Epsilon (ɛ): Max radius of the neighborhood. Data points will be valid neighbors if their mutual distance is less than or equal to the specified epsilon. In other words, it is the distance that DBSCAN uses to determine if two points are similar and belong together.
How to choose optimal EPs for DBSCAN cross?
Plot the chart of distances on Y-axis v/s the index of the datapoints on X-axis. Observe the sudden increase or what we popularly call as an ‘elbow’ or ‘knee’ in the plot. Select the distance value that corresponds to the ‘elbow’ as optimal eps.