Encountering ensemble learning algorithm in winning solutions of data science competitions has become a norm now. The ability to train multiple learners on a set of hypothesis, not only adds robustness to the model, but also enables it to deliver highly accurate predictions. In case you missed, I would recommend reading Basics of Ensemble Learning Explained in Simple English before you go forward.

While building ensemble models, one of most common challenge that people face is to find optimal weights. Few of them fight hard to solve this challenge and the not so brave ones, convince themselves to apply simple bagging, which assumes equal weight for all models and takes the average of all predicted values.

It often works well because it removes variance error from individual models. However, as we know, assigning equal weights is not the best approach to obtain the best combination of models. What could be an alternate way? **In this article, I’ve solved the problem of finding out the optimal weights in ensemble learners using neural network** in R Programming.

Imagine you have built 3 models on a given (hypothetical) data set. Each of these models predicts probability of output for an event.

In the following figure : Model 1, Model 2 and Model 3 are the three predictive models. Each of them have their own uniqueness and can work as a team to perform even better than individually best model.

We initially assume a 33.33% weight for each of the model and build an Ensemble model. Here, the challenge is to optimize these weights w1, w2 and w3 in such a fashion as to build a highly powerful ensemble model.

Assume p1 , p2 and p3 are three outputs from the three models respectively. We need to optimize w1, w2 and w3 to optimize an objective function. Let’s try to write down the constraints and objective function mathematically :

**Constraint :**

w1 + w2 + w3 = 1

p = w1 * p1 + w2 * p2 + w3 * p3

**Objective function (Maximize Likelihood function) :**

Maximize (Product over all observations [ (p) ^ (y) * (1-p) ^ (1-y) ] )

where y is the response flag for the observation p is the predicted probability through ensemble model p1, p2 and p3 : predicted probabilities from individual model w1, w2 and w3 : weights given to each model

This is a classic case of simplex optimization. However, with a huge number of models to bag, and diving into mathematical formulas every time can be stressful and time consuming at times. Hence, we need a smarter method here.

We’ll now learn to find these weights without getting into such mathematical formulation using neural network implementation.

I understand, neural network implementation can be quite overwhelming at times. Hence, to solve the current case in hand, we’ll not deep dive into the complex concepts of deep neural network.

Basically, neural network operates on finding weights for interlink between ( input variable to hidden node) and ( hidden node to output node). Our objective here is to find the right combination of weights from input nodes directly to the output node.

Here is an easy and diligent way to accomplish this task. We restrict the number of hidden nodes to one. This will automatically adjust the weight from hidden node to output node as 1. This implies that the hidden node and the output node is no longer different.

Here is a simplistic schema to represent what we just discussed :

So we found a way! This will automatically accomplish what we were trying to do using simplex equations. Let’s put this formulation down to a R code

# x is a matrix of multiple predictions coming from different learners #y is a vector of all output flags #x_test is a matrix of multiple predictions on an unseen sample x <- as.matrix(prediction_table) y <- as.numeric(output_flags) nn <- dbn.dnn.train(x,y,hidden = c(1), activationfun = "sigm",learningrate = 0.2,momentum = 0.8) nn_predict <- n.predict(nn,x) nn_predict_test <- n.predict(nn,x_test)

I have implemented this logic on multiple Kaggle problems and found that the results are quite encouraging. Here is one such example from a recent competition:

- Simple logistic model at score of 100
- Best machine learning model at score of 107
- Simple bagging techniques of 110, and finally
- Optimized weights using neural network of 113.

In this article, I’ve explained the method to find optimal weight in an ensemble model using a traditional approach and neural network implementation (recommended).

Did you find this article useful? Have you tried anything else to find optimal weights? I’ll be happy to hear from you in the comments section below.

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Understanding Cost Function
Understanding Gradient Descent
Math Behind Gradient Descent
Assumptions of Linear Regression
Implement Linear Regression from Scratch
Train Linear Regression in Python
Implementing Linear Regression in R
Diagnosing Residual Plots in Linear Regression Models
Generalized Linear Models
Introduction to Logistic Regression
Odds Ratio
Implementing Logistic Regression from Scratch
Introduction to Scikit-learn in Python
Train Logistic Regression in python
Multiclass using Logistic Regression
How to use Multinomial and Ordinal Logistic Regression in R ?
Challenges with Linear Regression
Introduction to Regularisation
Implementing Regularisation
Ridge Regression
Lasso Regression

Introduction to Stacking
Implementing Stacking
Variants of Stacking
Implementing Variants of Stacking
Introduction to Blending
Bootstrap Sampling
Introduction to Random Sampling
Hyper-parameters of Random Forest
Implementing Random Forest
Out-of-Bag (OOB) Score in the Random Forest
IPL Team Win Prediction Project Using Machine Learning
Introduction to Boosting
Gradient Boosting Algorithm
Math behind GBM
Implementing GBM in python
Regularized Greedy Forests
Extreme Gradient Boosting
Implementing XGBM in python
Tuning Hyperparameters of XGBoost in Python
Implement XGBM in R/H2O
Adaptive Boosting
Implementing Adaptive Boosing
LightGBM
Implementing LightGBM in Python
Catboost
Implementing Catboost in Python

Introduction to Clustering
Applications of Clustering
Evaluation Metrics for Clustering
Understanding K-Means
Implementation of K-Means in Python
Implementation of K-Means in R
Choosing Right Value for K
Profiling Market Segments using K-Means Clustering
Hierarchical Clustering
Implementation of Hierarchial Clustering
DBSCAN
Defining Similarity between clusters
Build Better and Accurate Clusters with Gaussian Mixture Models

Introduction to Machine Learning Interpretability
Framework and Interpretable Models
model Agnostic Methods for Interpretability
Implementing Interpretable Model
Understanding SHAP
Out-of-Core ML
Introduction to Interpretable Machine Learning Models
Model Agnostic Methods for Interpretability
Game Theory & Shapley Values

Deploying Machine Learning Model using Streamlit
Deploying ML Models in Docker
Deploy Using Streamlit
Deploy on Heroku
Deploy Using Netlify
Introduction to Amazon Sagemaker
Setting up Amazon SageMaker
Using SageMaker Endpoint to Generate Inference
Deploy on Microsoft Azure Cloud
Introduction to Flask for Model
Deploying ML model using Flask

Hi Tavish, I found this article very interesting. However I need some more understanding on the matrices x,y and x_test used in your code. For example I have 3 models and their corresponding predicted values in the following format: Actual Value | Predicted Value by Model1 | Predicted Value by Model2 | Predicted Value by Model3 A | A1 | A2 | A3 B | B1 | B2 | B3 C | C1 | C2 | C3 - - - In such a case what will be the contents of x,y and test_x?

Hi Samrat, In this case x will be A1,A2,A3 and y will be column A. To build test you need an unseen population. Hope this clarifies. Tavish

Hi Tavish, Excellent Write-up. i have a Quick question. How to set activation function for Numerical Predicted values( it might be Time Series or Regression Output). i had tried "linear" and "tanh" also. but i 'm getting only Categorical Output. like 1 or 0. Please help me this out

Hi Karthi, If you are using neural net package, then using the code mentioned in the article will give you probability prediction.

Thanks for the explanation Tavish. I'm not familiar with the R package. " nn <- dbn.dnn.train(x,y,hidden = c(1), activationfun = "sigm",learningrate = 0.2,momentum = 0.8) " According to the code above, the activiationfun is "sigm" for both hidden node and output node. And the weight W from hidden node to output node is fixed to 1 , which means W will not be updated during the backpropagation. Am I right ? or the network setting is not like this?

Thanks lot Tavish.

Trying to implement this logic for my model. Iam not able to find weights w1,w2,w3,... for the model in dbn.dnn.train. I understand number of weights should be the same as number of columns of predictions given by different models. Searching documentation of dbn.dnn.train doesnt give "Values" description which is normally given in other packages. Please give a pointer on this.