A month back, I participated in a Kaggle competition called TFI. I started with my first submission at 50th percentile. Having worked relentlessly on feature engineering for more than 2 weeks, I managed to reach 20th percentile. To my surprise, right after tuning the parameters of the machine learning algorithm I was using, I was able to breach top 10th percentile.

This is how important tuning these machine learning algorithms are. Random Forest is one of the easiest machine learning tool used in the industry. In our previous articles, we have introduced you to Random Forest and compared it against a CART model. Machine Learning tools are known for their performance.

Random forest is an ensemble tool which takes a subset of observations and a subset of variables to build a decision trees. It builds multiple such decision tree and amalgamate them together to get a more accurate and stable prediction. This is direct consequence of the fact that by maximum voting from a panel of independent judges, we get the final prediction better than the best judge.

We generally see a random forest as a black box which takes in input and gives out predictions, without worrying too much about what calculations are going on the back end. This black box itself have a few levers we can play with. Each of these levers have some effect on either the performance of the model or the resource – time balance. In this article we will talk more about these levers we can tune, while building a random forest model.

Parameters in random forest are either to increase the predictive power of the model or to make it easier to train the model. Following are the parameters we will be talking about in more details (Note that I am using Python conventional nomenclatures for these parameters) :

There are primarily 3 features which can be tuned to improve the predictive power of the model :

These are the maximum number of features Random Forest is allowed to try in individual tree. There are multiple options available in Python to assign maximum features. Here are a few of them :

*Auto/None*: This will simply take all the features which make sense in every tree.Here we simply do not put any restrictions on the individual tree.*sqrt*: This option will take square root of the total number of features in individual run. For instance, if the total number of variables are 100, we can only take 10 of them in individual tree.”log2″ is another similar type of option for max_features.*0.2*: This option allows the random forest to take 20% of variables in individual run. We can assign and value in a format “0.x” where we want x% of features to be considered.

**How does “max_features” impact performance and speed?**

Increasing max_features generally improves the performance of the model as at each node now we have a higher number of options to be considered. However, this is not necessarily true as this decreases the diversity of individual tree which is the USP of random forest. But, for sure, you decrease the speed of algorithm by increasing the max_features. Hence, you need to strike the right balance and choose the optimal max_features.

This is the number of trees you want to build before taking the maximum voting or averages of predictions. Higher number of trees give you better performance but makes your code slower. You should choose as high value as your processor can handle because this makes your predictions stronger and more stable.

If you have built a decision tree before, you can appreciate the importance of minimum sample leaf size. Leaf is the end node of a decision tree. A smaller leaf makes the model more prone to capturing noise in train data. Generally I prefer a minimum leaf size of more than 50. However, you should try multiple leaf sizes to find the most optimum for your use case.

There are a few attributes which have a direct impact on model training speed. Following are the key parameters which you can tune for model speed :

This parameter tells the engine how many processors is it allowed to use. A value of “-1” means there is no restriction whereas a value of “1” means it can only use one processor. Here is a simple experiment you can do with Python to check this metric :

%timeit

model = RandomForestRegressor(n_estimator = 100, oob_score = TRUE,n_jobs = 1,random_state =1)

model.fit(X,y)

Output ———- 1 loop best of 3 : 1.7 sec per loop

%timeit

model = RandomForestRegressor(n_estimator = 100,oob_score = TRUE,n_jobs = -1,random_state =1)

model.fit(X,y)

Output ———- 1 loop best of 3 : 1.1 sec per loop

“%timeit” is an awsum function which runs a function multiple times and gives the fastest loop run time. This comes out very handy while scalling up a particular function from prototype to final dataset.

This parameter makes a solution easy to replicate. A definite value of random_state will always produce same results if given with same parameters and training data. I have personally found an ensemble with multiple models of different random states and all optimum parameters sometime performs better than individual random state.

This is a random forest cross validation method. It is very similar to leave one out validation technique, however, this is so much faster. This method simply tags every observation used in different tress. And then it finds out a maximum vote score for every observation based on only trees which did not use this particular observation to train itself.

Here is a single example of using all these parameters in a single function :

model = RandomForestRegressor(n_estimator = 100, oob_score = TRUE, n_jobs = -1,random_state =50, max_features = "auto", min_samples_leaf = 50)

model.fit(X,y)

We have referred to Titanic case study in many of our previous articles. Let’s try the same problem again. The objective of this case here will be to get a feel of random forest parameter tuning and not getting the right features. Try following code to build a basic model :

Machine learning tools like random forest, SVM, neural networks etc. are all used for high performance. They do give high performance, but users generally don’t understand how they actually work. Not knowing the statistical details of the model is not a concern however not knowing how the model can be tuned well to clone the training data restricts the user to use the algorithm to its full potential. In some of the future articles we will take up tuning of other machine learning algorithm like SVM , GBM and neaural networks.

Have you used random forest before? What parameters did you tune? How did tuning the algorithm impact the performance of the model? Did you see any significant benefits by doing the same? Do let us know your thoughts about this guide 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

10 Nov 23 • 08:00pm

Brilliantly written article. Currently I have used all of these techniques in a Data science problem I was working on and it definitely helps in improving model performance and accuracy. Recently,I came across something else also when I was reading some articles on Random Forest, i.e a Regularization of Random Forest. The theme was to only split data with some variables if the splitting is significant enough using Statistical validation, now this is something which can help in taking Random Forest to next level, as It can help in reducing over-fitting. I tried to use it using R caret package but I think this technique is computationally expensive so couldn't run it over my system. I would love to see an article on it to understand it's working and how its performance can be improved.

Hi Tavish, Very useful article.

Hi karthi, Thanks for the appreciation.

I love AV and am a fan of your articles. I have heard something like Conditional Inference Trees which are similar to Random Forests. Can you share your thoughts on Conditional Inference Trees also? How does it work & its tuning parameters, when does it outcast Random Forests?

Great article! I would love to see something similar regarding parameter tuning for the XGBoost package.

This was a very nice article. I would still be interested to know if there is a minimum number of trees that can be calculated to reduce computational cost?

Perfect! This is exactly what I was looking for. Thanks for sharing.

I usually get confused about this topic. Very well explained. thanks a lot.