Gradient Boosting in Classification

Gradient Boosting in Classification

The category Gradient Boosting is located under Machine Learning in Classification on the feature studio. Alternatively, use the search bar to find the Gradient Boosting test feature. Use the drag-and-drop method or double-click to use the algorithm in the canvas. Click the algorithm to view and select different properties for analysis.

  

Gradient Boosting is a robust machine learning algorithm in the ensemble learning family. It integrates several weak predictive models, often decision trees, to produce a powerful predictive model. For problems involving classification and regression, Gradient boosting is highly successful.
The "Gradient" in Gradient boosting uses Gradient descent optimization to minimize the loss function. In each iteration, the algorithm calculates the negative Gradient of the loss function concerning the predicted values. This Gradient represents the direction in which the loss function decreases the fastest. The weak model is then trained to expect this Gradient, and the resulting predictions are added to the ensemble.
The boosting aspect of Gradient boosting comes from the fact that the weak models are combined sequentially. Each new model is trained with a focus on improving the ensemble's performance by targeting instances that were previously poorly predicted. The predictions from all weak models are combined using a weighted sum. The weights assigned to each model are typically determined based on their performance.

Properties of Gradient Boosting

 


Field

Description

Remark

RunIt allows you to run the node.-
ExploreIt allows you to explore the successfully executed node.-
Vertical Ellipses

The available options are

  • Run till node
  • Run from node
  • Publish as a model
  • Publish code
-

Task Name

It is the name of the task selected on the workbook canvas.

You can click the text field to edit or modify the task name as required.

Dependent Variable

It allows you to select the dependent variable

You can choose only one variable, which should be Categorical type.

Independent Variable


It allows you to select the independent variable.

  • You can select more than one variable
  • You can select variables of any type

Advanced






Loss Function

It allows you to choose from 2 options: deviance & exponential

It is a function that maps an event or values of one or more variables onto an actual number.

Learning Rate

It allows you to change the learning rate accordingly

It is a tuning parameter in an optimization algorithm that determines the step size at each iteration while moving towards a minimum of a loss function

Number of estimators

It allows you to select the number of estimators.

It is an equation for picking the best or most likely accurate data model based on observations.

Maximum Depth

It allows you to select the amount of the maximum depth.

It refers to the maximum number of levels or layers created in the boosting process.

Split Criterion

It allows you to select any of the options in the box.

It is a method to evaluate and select the best-split points when constructing decision trees within the boosting process.

Random State

It allows you to enter the value of the random state.

It refers to a parameter controlling the random number generation during training.

Example of Gradient Boosting 

Here, we apply Gradient Boosting to the Female birth dataset in the example below. The independent variable is Births. The dependent variable is "Location."
 

The figure given below displays the input data:

After using Gradient Boosting, the following results are displayed according to the Event of Interest

 

The result page displays the following sections.

  1. Key Performance Index
  2. Confusion Matrix
  3. Graphical Representation

Section 1 – Key Performance Index (KPI)

  

The Table given below describes the various parameters present on the Key Performance Index:

Field

Description

Remark

Accuracy

Accuracy is the ratio of the total number of correct predictions made by the model to the full predictions.
Accuracy = (TP + TN) / (TP + TN + FP + FN)
Where,
TP, TN, FP, and FN indicate True Positives, True Negatives, False Positives, and False Negatives, respectively.

The Accuracy is 0.5014.

F-Score

F-score is a measure of the accuracy of a test.
It is the harmonic mean of the test's precision and recall.

F-score = 2 (precision × recall) / (precision + recall)
Where,
precision = positive predictive value, which is the proportion of the positive values that are positive.
Recall = sensitivity of a test, the test's ability to correctly identify positive results to get the true positive rate.

It is also called the F-measure or F1 score.
The F-score is 0.5507.

Precision

Precision is the ratio of the True positive to the sum of the True positive and False Positive. It represents positive predicted values by the model.

The precision for No is 0.4798

Sensitivity

It measures the test's ability to identify positive results correctly.
Sensitivity = TP / (TP + FN)
Where,
TP = number of true positives
FN = number of false negatives

It is also called the True Positive Rate.
The value of sensitivity is 0.6463

Specificity

It gives the ratio of the correctly classified negative samples to the total negative pieces.
Specificity = TN / (TN + FP)
Where,
TN = number of true negatives
FP = number of false positives

It is also called inverse recall.
The value of specificity is 0.5275


Section 2 – Confusion Matrix

The Confusion Matrix obtained is given below:

The Table given below describes the various values present in the Confusion Matrix:
Let us calculate the TP, TN, FP, and FN values for the class Delhi.

Field

Description

Remarks

Predicted

It provides the predicted values from the classification model.

Here the predicted values for Delhi are 95.

Actual

It gives the actual values from the result.

Here the actual values for Delhi are 95.

True Positive

It gives the number of results that are genuinely predicted to be positive. The Actual Value and the predicted value should be the same.

Here TP value is 95.

True Negative

It gives the number of results that are genuinely predicted to be negative. The sum of all the columns and rows except the values of that class that we are calculating the values for.

Here the TN value isTN = ( 81 + 23 + 4 + 7 )= (115)

False Positive

It gives the number of results that are falsely predicted to be positive. The sum of the values of the corresponding columns except TP value.

Here FP value isFP = ( 63 + 40 )= ( 103 )

False Negative

It gives the number of results that are falsely predicted to be negative. The sum of values of the corresponding rows except for the TP value.

Here FN value is FN = ( 46 + 6 )= ( 52 )

Section 3 – Geographical Representation

The Receiver Operating Characteristic (ROC) Chart for the Event of Interest is given below:

The Lift Chart obtained for the Event of Interest is given below:

The Table given below describes the ROC Chart and the Lift Curve:

Field

Description

Remark

ROC Chart

The ROC curve is a probability curve that helps measure the performance of a classification model at various threshold settings.

The ROC curve is plotted with True Positive Rate on the Y-axis and False Positive Rate on the X-axis.
We can use ROC curves to select the most optimal models based on the class distribution.
The dotted line is the random choice with a probability equal to 50%, an Area Under Curve (AUC) equal to 0.7995, and a slope equal to 1.

Lift Chart

A lift is the measure of the effectiveness of a model.
It is the ratio of the percentage gain to the percentage of random expectation at a given docile level.
It is the ratio of the result obtained with a predictive model to that obtained without it.

A lift chart contains a lift curve and a baseline.
The curve should go as high as possible towards the top-left corner of the graph.


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