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

Why Central Limit Theorem is Important for evey Data Scientist?

The Central Limit Theorem is at the core of what every data scientist does daily: make statistical inferences about data. The theorem gives us the ability to quantify the likelihood that our sample will deviate from the population without having to take any new sample to compare it with. We don’t need the characteristics about the whole population to understand the likelihood of our sample being representative of it. The concepts of confidence interval and hypothesis testing are based on the CLT. By knowing that our sample mean will fit somewhere in a normal distribution, we know that 68 percent of the observations lie within one standard deviation from the population mean, 95 percent will lie within two standard deviations and so on. In other words we can say " It all has to do with the distribution of our population. This theorem allows you to simplify problems in statistics by allowing you to work with a distribution that is approximately normal." The CLT is...

Data is the New oil of Industry?

Let's go back to 18th century ,when development was taking its first footstep.The time when oil was considered to be the subset of industrial revolution. Oil than tends to be the most valuable asset in those time. Now let's come back in present. In 21st century, data is vigorously called the foundation of information revolution. But the question that arises is why are we really calling data as the new oil. Well for it's explanation Now we are going to compare Data Vs Oil Data is an essential resource that powers the information economy in much the way that oil has fueled the industrial economy. Once upon a time, the wealthiest were those with most natural resources, now it’s knowledge economy, where the more you know is proportional to more data that you have. Information can be extracted from data just as energy can be extracted from oil. Traditional Oil powered the transportation era, in the same way that Data as the new oil is also powering the emerging transportation op...

Math Skills required for Data Science Aspirants

The knowledge of this essential math is particularly important for newcomers arriving at data science from other professions, Specially whosoever wanted to transit their career in to Data Science field (Aspirant). Because mathematics is backbone of Data science , you must have knowledge to deal with data, behind any algorithm mathematics plays an important role. Here am going to iclude some of the topics which is Important if you dont have maths background. 1. Statistics and Probability 2. Calculus (Multivariable) 3. Linear Algebra 4. Methods for Optimization 5. Numerical Analysis 1. Statistics and Probability Statistics and Probability is used for visualization of features, data preprocessing, feature transformation, data imputation, dimensionality reduction, feature engineering, model evaluation, etc. Here are the topics you need to be familiar with: Mean, Median, Mode, Standard deviation/variance, Correlation coefficient and the covariance matrix, Probability distribution...

Blog | Data Science and Technology

Search This Blog