Random Forest

 

What is Machine Learning?

Machine learning is an artificial intelligence (AI) technology that allows systems to learn and improve without having to be explicitly programmed. Machine learning is the study of how to create computer programs that can access data and learn for themselves. Learning starts with observations or data, such as examples, direct experience, or teaching, so that we may seek for patterns in data and make better judgments in the future based on the examples we offer. The main goal is for computers to learn on their own, without the need for human involvement, and change their behaviour accordingly.

Types of Machine Learning.

Machine learning algorithms may be trained in a variety of methods, each with its own set of benefits and drawbacks. To comprehend the benefits and drawbacks of each form of machine learning, we must first consider the type of data they consume. There are two types of data in machine learning: labelled data and unlabeled data. Labelled data includes both the input and output parameters in a machine-readable manner, however, labelling the data takes a lot of human effort, to begin with. Only one or none of the parameters are machine-readable in unlabeled data. This eliminates the need for human labour, but it necessitates more sophisticated solutions.

There are other sorts of machine learning algorithms that are employed in very specialized use-cases, but nowadays there are three primary techniques.

a) Supervised Learning

One of the most fundamental forms of machine learning is supervised learning. The machine learning algorithm is trained on labelled data in this case. Despite the fact that proper labelling of data is required for this approach to operate, supervised learning may be highly effective when utilized under the right conditions. Supervised learning problems can be further grouped into regression and classification problems.

• Classification: A classification problem is when the output variable is a category, such as red or blue or disease and no disease.
• Regression: A regression problem is when the output variable is a real value, such as dollars or weight.

b) Unsupervised Learning

The ability to deal with unlabeled data is a benefit of unsupervised machine learning. This implies that no human labour is necessary to make the dataset machine-readable, allowing the software to work on much bigger datasets. Because there are no labels to deal with in unsupervised learning, hidden patterns emerge. The program perceives relationships between data points in an abstract fashion, with no human input necessary.

c) Reinforcement Learning

Reinforcement learning is directly inspired by how people learn from data in their daily lives. It includes a trial-and-error algorithm that improves upon itself and learns from different scenarios.

Decision Tree.

Decision Tree Analysis is a general-purpose predictive modelling method with applications in a variety of fields. In general, decision trees are built using an algorithm that discovers alternative ways to partition a data set depending on certain circumstances. It is one of the most popular and useful supervised learning algorithms. Decision Trees are a supervised non-parametric learning approach that may be utilized for both classification and regression applications.

A decision tree is a tree-like graph with nodes representing where we choose an attribute and ask a question, edges representing the responses to the query, and leaves reflecting the actual output or class label. They're employed with a simple linear decision surface in non-linear decision making.

The examples are classified using decision trees by sorting them along the tree from the root to a leaf node, with the leaf node supplying the classification to the example. Each node in the tree represents a test case for an attribute, with each edge descending from that node representing one of the test case's potential responses. This is a cyclical procedure that occurs for each subtree rooted at the new nodes.

There are some important terms to consider when we use the decision tree.

• Information Gain - The assessment of changes in entropy after segmenting a dataset based on a characteristic is known as information gain. It determines how much data a feature gives about a class. We divided the node and built the decision tree based on the value of information gained. The greatest information gain node/attribute is split first in a decision tree method, which always seeks to maximize the value of information gain. 
• Entropy - Entropy is a metric for determining the degree of impurity in a particular characteristic. It denotes the unpredictability of data. 
• Gini Impurity - Gini Impurity is a metric used in the construction of Decision Trees to decide how a data set's characteristics should be divided into nodes to create the tree. A data set's Gini Impurity is a value between 0 and 0.5 that reflects the chance of new, random data being misclassified if it were given a random class label based on the data set's class distribution.

Random Forest.

A random forest is a machine learning approach for solving classification and regression problems. It makes use of ensemble learning, which is a technique for solving difficult problems by combining many classifiers. The random forest method determines the outcome based on decision tree forecasts. It forecasts by averaging the output of various trees. The precision of the result improves as the number of trees grows.

These are some features of the Random Forest:

• • It outperforms the decision tree algorithm in terms of accuracy.
• • It is a useful tool for dealing with missing data.
• • Without hyper-parameter tweaking, it can provide a fair result.
• • It overcomes the problem of decision tree overfitting.
• • At the node's splitting point in every random forest tree, a subset of characteristics is chosen at random.

Now we see how the Random Forest algorithm works.

A random forest algorithm's building pieces are decision trees. So that’s why I mentioned some details about decision trees above.

The primary distinction between the decision tree and the random forest algorithms is that the latter randomly establishes root nodes and segregates nodes. The bagging method is used by the random forest to generate the needed result. Rather than utilizing a single sample of data, bagging requires using several samples. A training dataset is a collection of observations and characteristics used to make predictions. Depending on the training data given to the random forest algorithm, the decision trees yield varied results. These outputs will be rated, and the one with the best score will be chosen as the final product.

As an example, Let's say we want to predict if a consumer would buy a phone or not. His choice is based on the phone's specifications. The ultimate output, whether purchasing or not buying, is represented by the leaf node. The price, internal storage, and RAM are the key factors that influence the decision.

Here, instead of a single decision tree, the random forest will contain a large number of them. Assume we only have four decision trees. The training data, which includes the phone's observations and characteristics, will be split into four root nodes in this scenario.

The root nodes might symbolize four characteristics that impact a customer's decision (price, internal storage, camera, and RAM). The nodes will be divided by the random forest, which will choose characteristics at random. The outcome of the four trees will determine the final result.

The ultimate choice will be determined by the conclusion picked by most decision trees. The ultimate result will be purchasing if three trees predict buying and one tree predicts not buying.

Various decision trees are used in a random forest system. There are three types of nodes in a decision tree: decision nodes, leaf nodes, and root nodes. Each tree's leaf node represents the ultimate result produced by that particular decision tree. The final product is chosen using a majority-voting mechanism. The random forest system's ultimate output is the output picked by the majority of decision trees.

Let’s discuss another example for getting an understanding of how the random forest works. A training dataset is available that includes phones from Samsung, Apple, Huawei, and OnePlus. This dataset is divided into subgroups by the random forest classifier. Every decision tree in the random forest system is given these subsets. Each decision tree generates its own output. The result for trees 1 and 2 is Samsung, for example. Another decision tree indicated that OnePlus will be the winner. The final prediction is made using the majority voting collected by the random forest classifier. Samsung has been picked as the result by the majority of decision trees. As a result, Samsung gets chosen as the final prediction by the classifier.

Applications of Random Forest.

a) Stock Market - It is used by financial experts to discover prospective stock markets. It also helps them to recognize the stock activity.

b) E-commerce - E-commerce firms may anticipate client preferences based on historical consumption behavior using random forest algorithms.

c) Banking - In banking, a random forest is used to forecast a loan applicant's creditworthiness. This assists the lending organization in making an informed choice about whether or not to grant the loan to the consumer. The random forest technique is frequently used by banks to detect fraudsters.

d) Health-care services - Random Forest algorithms are used by doctors to diagnose patients. Patients are diagnosed by looking at their past medical records. Previous medical data are examined in order to determine the proper dose for the patients.

Advantages of Random Forest.

• It is capable of both regression and classification.
• A random forest generates accurate forecasts that are simple to comprehend.
• It is capable of effectively handling big datasets.
• In comparison to the decision tree method, the random forest algorithm is more accurate in predicting outcomes.