Types of Machine Learning Algorithms and building Decision Tree Algorithms

In everyday life, analogies with trees are frequent. Trees, which are made out of roots, trunks, branches, and leaves, frequently represent growth. A decision tree is an algorithm used in machine learning that can build both classification and regression models.

Because it begins at the base, like an upside-down tree, and branches out to show different outcomes, the decision tree gets its name. Decision trees assist us in visualizing these models and modifying how we train them because machine learning is centered on the idea of solving issues.

What you need to know about decision trees in machine learning is provided here.

What is machine learning is it a decision tree?

Modeling decisions and results using decision trees is one technique to map decisions in a branching structure. Decision trees are used to estimate the likelihood that various iterations of decisions will be successful in achieving a particular goal. As a decision tree can be used to manually model operational decisions like a flowchart, the idea of a decision tree predates machine learning. They are frequently taught and used as a method of analyzing organizational decision-making in the business, economics, and operation management fields.

Decision trees are a type of predictive modeling that can be used to map several options or solutions to a certain result. Different nodes make up decision trees. The decision tree's root node, which in machine learning typically represents the entire dataset, is where it all begins. The leaf node is the branch's termination point or the result of all previous decisions. The decision tree won't branch out from a leaf node. With decision trees in machine learning, the core nodes represent the data's features, and the leaf nodes represent the results.

In supervised machine learning, which trains models using labeled input and output datasets, decision trees are a method employed. The method is mostly used to address classification issues, which include categorizing or classifying an object using a model. Regression issues are a machine learning application where decision trees are also used to predict outcomes from unobserved data.

Due to their ease of use and popularity in machine learning, decision trees are a common model structure. The model's decision-making process is also easily understood because of the tree-like structure. The process of communicating a model's output to a human is a significant consideration in machine learning. It is often challenging to describe a given model's output because machine learning excels at task optimization without direct human input. A decision tree structure makes it easier to understand the reasoning behind a model's decision-making process because each decision branch can be seen.

Terminologies for Decision Trees

• Root Node: The decision tree's root node is where it all begins. The full dataset is represented and split into two or more homogeneous sets.
• Leaf Node: Leaf nodes are the ultimate output nodes, after which the tree cannot be further divided.
• Splitting: The division of the decision node/root node into sub-nodes by the specified conditions is known as splitting.
• Branch/Sub Tree: A branch or subtree is a tree created by slicing another tree.
• Pruning:? Pruning is the procedure of removing the tree's undesirable branches.
• Parent/Child node:? The root node of the tree is referred to as the parent node, while the other nodes are referred to as the child nodes.

Decision trees' advantages in Machine Learning

For good reason, decision trees are a common machine learning strategy. Because it visualizes the decision-making process, the resulting decision tree is simple to comprehend. This simplifies the process of describing a model's output to stakeholders that lack specialized data analytics understanding. The model and data are visualized in a way that non-specialist stakeholders can access and comprehend, making the data available to many business teams. Thus, it is easy to comprehend the model's logic or reasoning. This is an obvious advantage for the usage of decision trees in machine learning since explainability can be a barrier to the adoption of machine learning within organizations.

The phase of data preparation for decision tree machine learning models offers another advantage. Compared to other machine learning models, decision tree models require fewer data cleansing. In particular, decision trees eliminate the necessity for data normalization during the initial stages of machine learning. Since decision tree models can handle both categorical and numerical data, they eliminate the requirement to alter qualitative variables as they would in other methods.

The following are the primary advantages of decision trees in machine learning:

• Simple to comprehend, especially for stakeholders that are unfamiliar with approach data.
• Intrinsically explicable because any model-based decision can be justified inside the model. In contrast, explainability becomes challenging for black box algorithms.
• Since the approach can handle both numerical and categorical variables, data doesn't need to be normalized.
• can be used to comprehend the feature hierarchy inside a dataset, which can then be edited and improved upon in upcoming modeling.

Decision trees' shortcomings in machine learning

The problem of overfitting is one of the key disadvantages of utilizing decision trees in machine learning. Machine learning models strive to attain a trustworthy level of generalization such that, once deployed, the model can reliably analyze unknown data. When a model is overfitted to the training data, it may lose accuracy when dealing with fresh data or forecasting future events.

Decision trees frequently develop into very complicated and large structures, which can lead to overfitting as a serious problem. Pruning is a necessary step in decision tree refinement because it prevents overfitting. When a tree is pruned, branches and nodes that are unrelated to the model's objectives or that don't add any new information are removed. Any trimming should be evaluated via cross-validation in machine learning, which can assess the model's functionality or accuracy in a real-world setting.

The following are some of the primary drawbacks of decision trees in machine learning that should be taken into account:

• Decision trees need to be pruned and optimized because they might become exceedingly complex.
• Overfitting is a frequent problem that calls for additional hyperparameter pruning and optimization.
• Small changes to the training data can significantly impact the decision tree and frequently lead to the creation of distinct decision trees.
• The method, particularly with continuous numerical outputs, can be less accurate with regression issues than other machine learning algorithms.
• Machine learning models are subject to bias if the training dataset isn't fair or representative.