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Day 32 of #100dayswithmachinelearning
Topic - Encode Numerical Features ( Binning & Binarization )
A Thread 🧵
Topic - Encode Numerical Features ( Binning & Binarization )
A Thread 🧵
Discretization: It is process of transforming continuous variables into categorical variable by creating set of intervals, which are contiguous, that span over the range of the variable’s values. It is also known as “Binning”, where the bin is an analogous name for an interval
Benefits of Discretization or Binning :
1⃣ Handles the Outliers in a better way.
2⃣ Improves the value spread.
3⃣ Minimize the effects of small
observation errors.
1⃣ Handles the Outliers in a better way.
2⃣ Improves the value spread.
3⃣ Minimize the effects of small
observation errors.
Types of Binning:
(a) Unsupervised Binning:
1⃣Equal width binning: It is also known as “Uniform Binning” since the width of all the intervals is the same. The algorithm divides the data into N intervals of equal size
(a) Unsupervised Binning:
1⃣Equal width binning: It is also known as “Uniform Binning” since the width of all the intervals is the same. The algorithm divides the data into N intervals of equal size
2⃣ Equal frequency binning: It is also known as “Quantile Binning”. The algorithm divides the data into N groups where each group contains approximately the same number of values.
3⃣ K-means binning: This technique uses the clustering algorithm namely ” K-Means Algorithm”.
This technique is mostly used when our data is in the form of clusters.
This technique is mostly used when our data is in the form of clusters.
(b) Custom binning: It is also known as “Domain” based binning. In this technique, you have domain knowledge about your business problem statement and by using your knowledge you have to do your custom binning.
▶️Binarization: It is a special case of Binning Technique. In this technique, we convert the continuous value into binary format i.e, in either 0 or 1.
Very useful Technique in Image Processing, for converting a colored image into a black and white image.
Very useful Technique in Image Processing, for converting a colored image into a black and white image.
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