Decision trees are frequently used by businesses to highlight the progress done in the decision-making process. As the planning and plotting are done for particular decision points are added from time to time to state the outcome or the final results of those decisions. Finally, a tree-like structure or a visual flowchart comes up pointing out how a decision started, what new inputs were added, and what was the end of those decisions.
Businesses make use of decision trees and machine learning models for structuring the algorithm. Then the decision tree algorithms are further used to split the database features by using a cost function. Notably, the decision tree is first grown and then optimized to remove the branches which are irrelevant and not required. This process is termed pruning. Different parameters like the depth of the decision tree can be set to lower the risk of overfitting and becoming an overly complex tree. A simple tree would be easier to understand for all the other employees in the company apart from the ones who have made it.
What is the main function of a Decision Tree in Machine Learning?
Most of the decision trees in machine learning are used for classifying the problems and categorization the objects against the learned features. This approach can also be used for regression problems or it can be used as a method for predicting possible outcomes from the unseen data. What is the prime reason for using a decision tree in machine learning? By making a decision tree it becomes easier to visualize the decision-making process and put it into practice. Another important benefit is the simplicity, each step of the thought process and the possible paths leading to its implementation can be shown on the decision tree. It should be noted that machine learning can become overly complex when granular branches are generated. The pruning of the tree from time to time can help in solving this hurdle.
In this article, you will further know about various decision trees in machine learning, the possible benefits and drawbacks of this approach, and what different kinds of decision trees in machine learning are.
Get to know all about the Decision Tree in Machine Learning
Decision trees are helpful in calculating what can be the outcome of different decisions. Will these decisions bring out something positive or their result will be negative for a business? How can the decisions be used for achieving a goal? All these questions are answered when a decision tree is made and thought over. Decision trees can be called a way of modelling decisions, calculating their outcomes, and mapping the decisions in a particular branching structure. The concept of the decision tree is not new and it existed much before machine learning came to light. The decision tree can be used to list the operational decisions manually in a flowchart. Most businesses not only utilize decision trees but they also teach their employees how to make one and reap benefits from it. The economics and operation management sectors are the ones that use decision trees to analyze organizational decision-making.
Decision trees can be seen as a form of predictive modelling that helps a business in mapping different decisions to the possible outcome. It is of different nodes that a decision tree is made of. The decision tree starts with the root node and this is what the whole dataset within the machine learning is. Next comes the leaf nodes, they are said to be the endpoint of the branch or we can also call them the final outcomes coming out of the series of decisions that have been listed. After the leaf node, the decision tree doesn’t go any further. In terms of machine learning, the decision trees contain the features of data as the internal node, and the outcome is seen as the leaf mode.
Further, moving on what kind of approach do decision trees follow? These flowcharts are used in supervised machine learning, a technique that makes use of labelled inputs and different output datasets to train models. It is one of the best ways to solve classification problems like the models are used to categorize or classify an object. Moreover, these decision trees also help in the machine learning regression model. It is an approach used in predictive analytics to formulate and forecast output from hidden data.
The popularity of decision trees in machine learning is mainly because they are simple and easy to understand. The final model of the decision-making process is legible to anyone who takes a look at it. Utmost importance is given to self-explanation in machine learning. The flowcharts are quite clear and no complexity is involved in them. Anyone can be told about what the model’s outcome is going to be. The main strength with regard to machine learning is the easy optimization of a particular task. This should be done without any direct human interference. If the decision tree is not used it becomes difficult to decipher the outcome of the decision. But the reasoning involved in the decision-making process in a model gets clearer with the use of a decision tree structure. Since each decision branch is clearly visible and can be observed, the final outcome is quicker.
What is the different type of Decision Tree in Machine Learning?
There are different types of decision trees in machine learning. Most of the models are a part of two approaches to machine learning. One is supervised machine learning and the other is unsupervised machine learning. The most important difference which can be seen between these two approaches is what is a condition the training data is in. And what problem the model is being used to solve. Additionally, supervised machine learning models are usually used for classifying objects or data points as is often done in facial recognition software. The models are also used to predict continuous outcomes as is seen in stock forecasting tools. When it comes to unsupervised machine learning models, they are basically used to cluster the available data in grouping which consists of similar data points. It is even used to find association rules between the present variables as we can see in automated recommendation systems.
The decision trees that are used in the supervised type of machine learning are used in solving both regressions as well as classification problems. Henceforth we can say that machine learning includes two main types of decision trees which are classification trees and regression trees. The classification trees are the ones that are mainly used in decision trees in machine learning and they are also used to solve regression problems as well. There may be other differences as well but the main one lies in the type of problem and what is the available data.
The decisions in which the final answer required is a yes or a no are done with the help of classification trees. While the regression trees are mainly used for continuous outcome variables such as a number.
What is a classification tree?
The decision trees in machine learning are frequently used in classification problems. It is nothing but a supervised machine learning problem in which the model has already been trained for classifying the object and knowing whether it is a part of the known object class. The models which are used are generally trained in the process of assigning the class labels to the processed data. All these classes are learned by the model by the use of processing labelled training data present in the training part of the machine learning model lifecycle.
For effectively solving a classification problem, it is a must for a model to understand features that are going to categorize a data point and put it further into different class labels. When it comes to practice, the classification problem usually comes under a range of settings. The different examples include the classification of the documents, the available image recognition software, or even email spam detection.
In simpler terms, a classification tree is nothing but a way in which a model is structured for classifying objects or data. In the classification tree, the leaves or endpoints of branches are said to be the class labels. It is the point at which the branches stop splitting. The classification tree is generated incrementally and the complete dataset is broken into small subsets. It is applied to use when targeting variables present are either discrete or they are categorical and the branching is done by binary partitioning. For example, it is possible for each node to branch into a yes or no as an answer. The classification trees are made when either when the target variable is likely to be categorical or it can be given a specific category like a yes or a no. Therefore, the endpoint of each category can be said as one category.
What is a Regression Tree?
The regression problems are the ones when specific models are designed for forecasting or predicting a continuous value like predictions regarding house prices or about the price changes in the stock market. This technique is applied to train a model for understanding the relationship between the independent variables and their outcome. The linear regression models get trained on the available training data and fit them in the supervised type of machine learning algorithm.
The machine learning regression models are set to train on the relationship that exists between output and the input data. Once the relationship has been deciphered the model can be used for forecasting the outcomes from unseen input data. The primary use of these regression models is to predict the future or also for emerging trends in a range of settings. Some examples of regression models are to forecast house prices, for future retail sales, or even for portfolio performance in machine learning for finance.
What are the benefits of decision trees in machine learning?
Machine learning employs decision trees because of the innumerable benefits it has on offer. The final decision tree is the best way to understand the results due to its visualization of the decision process. For stakeholders, the model’s output can be understood without having any specialized knowledge of data analytics. And for non- specialist stakeholders, it can help in understanding and visualizing the data. The data is easily accessible to all the business teams and also helps in cross-validation in machine learning. Moreover, the reasoning or the logic can be understood clearly.
Some other important benefits of decision-making in machine learning are:
- Easy to understand without any prior technical knowledge.
- Any decision within the model gets its explanation from the model itself. It is totally different as compared to black box algorithms which are difficult to explain.
- There is no need for normalization of data as the technique is able to process both numerical as well as categorical variables.
- It can also be used for understanding the hierarchy of features present in the dataset.
Drawbacks of using decision trees in machine learning
The biggest drawbacks of using a decision tree in machine learning python are:
- Overfitting is a frequently occurring issue that requires pruning and optimization of hyperparameters.
- The small tweaks can lead to a big impact on the decision tree and it can further result in the creation of a completely different decision tree.
- With regard to regression problems, this approach can prove to be less accurate in comparison to other machine learning techniques.
- It is prone to bias in machine learning models if the training dataset is not balanced.
Decision trees can be of great benefit and can also help organizations when they are used effectively while taking care of some important factors. Machine learning solutions are gaining a lot of importance in days to come and many employers are using them for the benefit of their business and also to give that extra edge to their employees.
