Random forest is a classifier that develops from decision trees. It actually consists of several decision trees. To classify a new instance, each decision tree provides a classification for the input data; Collects random forest classifications and predicts the highest turnout as an outcome. The input of each tree is the data sample of the original dataset.
In addition, a subset of features are randomly select among the optional features for growing trees in each node. Each tree is expand without pruning. Essentially, random forests enable a large number of weak or weakly co-associated classifiers to form a strong classifier.
Random forest is a supervised learning algorithm. The “forest” it creates, is a group of decision trees, usually train with the “bagging” method. The general idea of the bagging method is that combining learning models increases the overall result.
Random Forest Features
- It is obscure in accuracy among current algorithms.
- runs efficiently on large data bases.
- can handle thousands of input variables without removing variables.
- It predicts what variables are important in classification.
- This produces an internal estimate of the general error as the forest building progresses.
- It is an effective method for estimating missing data and maintains accuracy when a large proportion of data is missing.
How it works ?
A major advantage of random forest is that it can be utilize for both classification and regression problems, which make up most of the current machine learning systems. Look at random forests in taxonomy, because taxonomy is sometimes consider as the building block of machine learning.
Random forests almost contain hyperparameters such as the decoy tree or the bagging classifier.
Random forests add additional randomness to the model while growing trees. Instead of searching for the most important feature when segmenting a node, it searches for the best feature among a random subset of features. This results in a wider variety that typically results in a better model.
You can also make trees more random by using random thresholds for each feature instead of searching for the best possible range (as does a general decision tree).
Important Hyperparameters
Hyperparameters are used in random forests to either increase the predictive power of the model or to speed up the model. Let’s look at Scalance’s Hyperparameters on the underlying random forest function.
1. Increasing predictive power
First, there is the n_estimators hyperpert, which is the number of trees that the algorithm makes before the maximum poll or average of the predictors. Generally, Increase in a number of trees means increase in performance and stability, but it slows down the computation.
Another important hyperparam maximum is max_features, which is the maximum number of random forests a node considers to be divided. The scalar provides several options, all described in the documentation.
The last significant hyperparate is min_sample_leaf. It sets the minimum number of leafs required to split an internal node.
2. Speed up the model
N_jobs tells the hyperparameter engine how many processors are allow to be in use. If it has a value, it can use only one processor. The value of “-1” states that there is no limit.
Random_state replicates the output of the hyperparameter model. The model will always produce the same result if it has a fix value of random_state and if it is set as the same hyperparameter and the same training data.
Finally, there is oob_score (also known as oob sampling), which is a random forest cross-validation technique. In this example, about one-third of the data is not in use to train the model and can be use to evaluate its performance. These samples are the bag samples. This is similar to the leave-one-out-cross-validation method, but almost no additional computational burden goes with it.
Gini importance
Each time a node is partition, the gini impurity on the variable m is less than the criterion node for two descendant nodes. Sinear decreases for each individual variable on all trees in the forest provide a sharp variable significance that often corresponds to a measure of ordinal significance.
Advantages and disadvantage
The biggest advantage of Random One is its versatility. It can be for both regression and classification functions, and it is also easy to see the relative importance assign to input characteristics.
Random forest is also a very useful algorithm because the default hyperparameters that use it often produce a good prediction result. Hyperparameters are very simple to understand, and many of them are not.
One of the biggest problems of machine learning is overfitting, but most of the time this happens thanks to the random forest classifier. If there are enough trees in the forest, the classifier will not exceed the model.
In general, these algorithms are faster to train, but much slower to make predictions after being trained. More accurate prediction requires more trees, resulting in a slower model. In most real-world applications, the random forest algorithm is quite fast but there can certainly be situations where run-time performance is important and other approaches will be preferred.
Use case | Random Forest
The Random forest algorithm is widely use in various fields like banking, stock exchange, medicine and e-commerce.
For example, in finance, it is to explore the possibility of customers repaying their loans on time, or the services of the bank use more frequently. This domain is also use to detect fraudsters to scam the bank. In trading, the algorithm can be used to determine the future behavior of stocks.
In the healthcare domain it is used to identify the correct combination of components in medicine and analyze the patient’s medical history to identify diseases.
Random forests are used in e-commerce to determine whether customers will actually like the product.
written by: Vikas Bhardwaj
Reviewed By: Vikas Bhardwaj
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