# GBM - PDP

[Code Implementation Here](https://colab.research.google.com/drive/1SiKKQ5T9rgrYjtDVEEraN1QZxTnztDF8?usp=sharing)

### What is GBM?

* Gradient Boosting Machine is a machine learning algorithm that forms an ensemble of weakly predicted decision trees
* It constructs a forward stage-wise additive model by implementing gradient descent in function space
* Also known as MART (Multiple Additive Regression Trees) and GBRT (Gradient Boosted Regression Trees)

### Making the Model

**Dataset:** Pima Indians Diabetes; **Target:** Outcome

The data is trained by calling the GradientBoostingClassifier function from Scikit learn Library

**Accuracy:** 

### **Implementation of Interpretability**

For this model, we will interpret with Partial Dependence Plots. 

![](/files/-MEfamNr5AFv0CQi4nGm)

With just few lines of code, we can plot the PDPs for any dataset using the sklearn partial\_dependence library.

### Visualizations

**PDP for every feature**

![](/files/-MEfbIMVi0-utHTLah_d)

The above plot shows how change in output varies with variations in feature values. Some key points for interpretation from the above plots:

* As Pregnancies increase, the person's chances of becoming diabetic go up
* Higher the Glucose, higher the chances of person becoming diabetic
* BMI more than 25 increases an individuals chances of becoming diabetic

 **3-D PDPs**

![](/files/-MEfcp8yXjzuB6IpNZpE)

These plots show the combined effect of two features on the change in output. As seen above, a reduction in both - Insulin and DiabetesPedigreeFunction, results in negative change of a person being diabetic (nearing non-diabetic situation).

**PDP interact plot** 

![](/files/-MEfeHMoH8oWZu97hWG5)

The below plot shows the change output prediction (value inside square) for every combination of values between the features Insulin and DiabetesPedigreeFunction(values given by scale).


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