Random Forest Algorithm

Random Forest is an ensemble machine learning algorithm that follows the bagging technique. The base estimators in the random forest are decision trees. Random forest randomly selects a set of features that are used to decide the best split at each node of the decision tree.

Looking at it step-by-step, this is what a random forest model does:

1. Random subsets are created from the original dataset (bootstrapping).

2. At each node in the decision tree, only a random set of features are considered to decide the best split.

3. A decision tree model is fitted on each of the subsets.

4. The final prediction is calculated by averaging the predictions from all decision trees.

To sum up, the Random forest randomly selects data points and features and builds multiple trees (Forest).

Random Forest is used for feature importance selection. The attribute (.feature_importances_) is used to find feature importance.

Some Important Parameters:-

1. n_estimators:- It defines the number of decision trees to be created in a random forest.

2. criterion:- "Gini" or "Entropy."

3. min_samples_split:- Used to define the minimum number of samples required in a leaf node before a split is attempted

4. max_features: -It defines the maximum number of features allowed for the split in each decision tree.

5. n_jobs:- The number of jobs to run in parallel for both fit and predict. Always keep (-1) to use all the cores for parallel processing.

Comments

Most Used Algorithm by DataScientist

We will discuss mostly machine learning algorithms that are important for data scientists and classify them based on supervised and unsupervised roles. I will provide you an outline for all the important algorithms that you can deploy for improving your data science operations. Here is the list of top Data Science Algorithms that you must know to become a data scientist. Let’s start with the first one – 1. Linear Regression Linear Regression is a method of measuring the relationship between two continuous variables . The two variables are – Independent Variable – “x” Dependent Variable – “y” In the case of a simple linear regression, the independent value is the predictor value and it is only one. The relationship between x and y can be described as: y = mx + c Where m is the slope and c is the intercept. Based on the predicted output and the actual output, we perform the calculation 2. Logistic Regression Logistic Regression is used for binary classificat...

Data Analytics Interview Questions - Part 1

Q1. Python or R – Which one would you prefer for text analytics? We will prefer Python because of the following reasons: Python would be the best option because it has Pandas library that provides easy to use data structures and high-performance data analysis tools. R is more suitable for machine learning than just text analysis. Python performs faster for all types of text analytics. Q2. How does data cleaning plays a vital role in the analysis? Data cleaning can help in analysis because: Cleaning data from multiple sources helps to transform it into a format that data analysts or data scientists can work with. Data Cleaning helps to increase the accuracy of the model in machine learning. It is a cumbersome process because as the number of data sources increases, the time taken to clean the data increases exponentially due to the number of sources and the volume of data generated by these sources. It might take up to 80% of the time for just c...

Data Science Interview Questions -Part 2

1) What are the differences between supervised and unsupervised learning? Supervised Learning Unsupervised Learning Uses known and labeled data as input Supervised learning has a feedback mechanism Most commonly used supervised learning algorithms are decision trees, logistic regression, and support vector machine Uses unlabeled data as input Unsupervised learning has no feedback mechanism Most commonly used unsupervised learning algorithms are k-means clustering, hierarchical clustering, and apriori algorithm 2) How is logistic regression done? Logistic regression measures the relationship between the dependent variable (our label of what we want to predict) and one or more independent variables (our features) by estimating probability using its underlying logistic function (sigmoid). The image shown below depicts how logistic regression works: The formula and graph for the sigmoid function is as shown: 3) Explain the steps in making a deci...

Blog | Data Science and Technology

Search This Blog