RANDOM FOREST IN MACHINE LEARNING/PYTHON/ARTIFICIAL INTELLIGENCE

Random Forest

• Types of Ensemble Methods
• Assumptions of Random Forest
• Advantages of Random Forest
• Random Forest Algorithm Working
• Applications of Random Forest
• Random Forest Regression
• Advantages of Random Forest Regression
• Disadvantages of Random Forest Regression

Random forest stands as a well-known supervised machine learning algorithm that can address both classification and regression problems within ML. Operating on ensemble learning principles, it leverages the collective intelligence of multiple classifiers to tackle intricate problems. This method harnesses the strengths of various models, improving the overall performance of the learning system. In this blog, we learn about random forest algorithms.

By amalgamating predictions from multiple decision trees, the random forest algorithm effectively mitigates overfitting while enhancing accuracy. Individual decision trees inherently possess high variance. However, when these trees are integrated into a random forest machine-learning model in parallel, the resulting variance diminishes. This reduction occurs because each decision tree is trained on a specific sample of data, ensuring that the output relies not on a single tree but on multiple trees, thereby lowering the overall variance.

In random forest models, higher numbers of trees correspond to increased accuracy while concurrently preventing overfitting tendencies, establishing a robust and more reliable model.

Image source original

Real-World Example for Random Forest

Let’s suppose we go for a hiking trip and get lost in the dense forest. Now we need to identify the type of tree we are under to navigate back to safety. But we find there are so many different trees, in such cases Random Forest, can help us it works like a team of expert tree guides that can help us to go back to safety.

A random forest isn’t a single decision tree, but it is a collection of trees, like a whole forest of knowledge. In random forests each tree votes for the type of tree it thinks it is. The final classification is based on the majority vote – the most popular choice among the trees.

When a new tree (data point) comes along, it’s passed through each decision tree in the forest. Each tree then votes for the type of tree it thinks the current tree is. The final classification is based on the majority vote – the most popular choice among the trees.

The advantage of using this method is that if one tree gets confused by an oddity, the others can compensate. Let’s take an example that one tree predicts that the current tree has bumpy bark, and it gets fooled, but other trees also have diverse knowledge that can classify the current tree correctly based on other features.

Random Forest’s strength lies in its multitude of perspectives. It’s like having many experts and each expert has their way of analyzing the data. They collaborate to make a more robust and accurate prediction, just like you’d be more confident in our hike if multiple guides agreed on the type of tree.

Types of Ensemble Methods

There are many verity of ensemble learning methods; they are:

Bagging (Bootstrap Aggregating) – In this approach, training involves using multiple models on randomly selected subsets of the training data. Following this, predictions from each model are aggregated, typically through averaging.

Boosting – This technique works in a sequence similar to a series of models, where training occurs one after the other, with each subsequent model dealing with the errors of the previous one. In this method, forecasts are combined through a weighted voting system.

Stacking – In this approach, the output from one model serves as input features for another model. Ultimately, the final prediction is derived from the second-level model.

Assumptions of Random Forest

Several decision trees are combined by a random forest technique to jointly forecast a dataset's class. Although individual trees in a forest may make incorrect predictions, the ensemble method ensures that most trees make accurate predictions. Let us now examine the two main assumptions behind the Random Forest classifier:

• For the Random Forest classifier in machine learning to make accurate predictions, it requires genuine values within the feature variables of the dataset rather than arbitrary or guessed values.
• Only when there is little to no correlation between the predictions given by the various trees can the Random Forest classifier perform successfully.

Why should we use Random Forest?

Below are some reasons or points that explain why we should use the Random Forest algorithm in machine learning:

• It takes a little amount of time to train when compared to rest algorithms.
• Its accuracy for prediction is high, and also with big datasets, it runs more efficiently.
• Furthermore, the Random Forest classifier can sustain its accuracy even in scenarios where a substantial part of the data is missing.

Random Forest Algorithm working

The two main random forest operations are the building phase and the prediction phase. During the construction phase, the algorithm builds a large number of decision trees, typically expressed as N trees. Random selections are made from a portion of the training data and feature set to create each decision tree. During the prediction stage, the algorithm generates predictions for every data point by utilizing the group of decision trees that were built during the first phase. Typically, before making a final prediction, all of the trees' projections are averaged or voted on. This rigorous process ensures that the random forest model can generate trustworthy predictions and resist variations in datasets.

First Step: Choose K randomly chosen data points from the training set to get started.

Second Step: Using the selected data subsets, decision trees are constructed in the second stage.

Third Step:  choose the number of decision trees (N) that you want to construct.

Fourth Step: Then, carry out steps 1 and 2 one more time.

Fifth Step: Get the projections from each decision tree and place the new data points in the most well-liked category.

To understand the working algorithm much better let’s look at one example.

Consider a dataset that contains several images depicting different fruits. These images are used as input for a machine-learning model that is constructed using a random forest classification technique. Using this strategy, the dataset is divided into smaller chunks, each of which is subjected to independent decision trees for analysis. Every decision tree generates a forecast when it is trained. When further data is added to the model, the Random Forest classifier predicts the result based on the output of the majority of the decision trees. This is an example of how this algorithm works.

Image source original

Applications of Random Forest

Let’s look at some applications of random forest where it is mostly used:

• Banking – it is used in the banking sector very much especially in the section of loan to check and identify the risk associated with it.
• Medicine – Using this algorithm, it becomes possible to discern disease patterns and assess the associated risks.
• Land Use – with the help of this algorithm we can identify the areas which have similar land.
• Marking – marking trends can be identified using this algorithm.
• Predicting continuous numerical values – This method can be employed to forecast various numerical outcomes such as housing prices, stock values, or the lifetime value of customers.
• Identifying risk factors – Additionally, it can identify risk factors for diseases, financial downturns, or other adverse occurrences.
• Handling high-dimensional data – because it uses decision trees inside it, therefore, it can analyze datasets that have quite a large number of features as input.
• Capturing complex relationships – Moreover, it can capture intricate connections between input features and target variables, enabling the modeling of complex relationships within the data.

What is Random Forest Regression?

Using ensemble techniques, random forest regression is one machine learning technique that combines regression and classification. Several decision trees and a procedure called Bootstrap Aggregation, or "bagging," are used in this strategy. Rather than depending just on one decision tree, a random forest combines several of them to produce the desired result.

An essential aspect of random forests is their utilization of multiple decision trees, each serving as an independent learning model. Through the Bootstrap method, sample datasets are generated for each model by randomly selecting rows and features from the original dataset. Predicting outcomes using Random Forest regression entails following standard procedures akin to other machine learning methodologies.

• Initially, we must formulate a precise question or specify the required data and identify the source from which to obtain the necessary data.
• We need to convert the data into an accessible format if it is not. 
• It's essential to identify and document all noticeable anomalies and missing data points within the dataset, as addressing these issues may be crucial for data quality and analysis purposes.
• Now we need to create a machine-learning model.
• For machine learning to work properly we need to establish a baseline model that we want to achieve.
• Following the data preprocessing steps, the next phase involves training the machine learning model using the prepared dataset.
• After training is done, we need to check its performance in unseen data or test data.
• Subsequently, it's essential to assess and compare the performance metrics between the test data and the model's predicted data.
• If the model’s performance does not achieve our expectations, we can try to improve it using tuning the hyperparameters or modeling the data with other techniques.
• At last, we interpret the data we have gained and report accordingly.

Out-of-bag score in Random Forest

The Out-of-Bag (OOB) score, also known as the Bag score, serves as a validation technique predominantly employed in bagging algorithms to assess their performance. This method involves extracting a small portion of validation data from the main dataset. Predictions are made on this specific validation subset, and the outcomes are subsequently compared with other results.

One significant benefit of the Out-of-Bag (OOB) score is its ability to evaluate the bagging algorithm's performance without using separate validation data. As a result, the OOB score provides an accurate assessment of the bagging algorithm's genuine performance.

To calculate the Out-of-Bag (OOB) score for a specific Random Forest algorithm, it is very important to set the OOB_Score parameter to "True" in the algorithm settings. This allows the algorithm to efficiently calculate and use OOB scores to evaluate its performance.

Advantages of Random Forest Regression

• We can easily use and it is less prone to be sensitive towards the training dataset compared to the decision tree.  
• It is more accurate as compared to a decision tree because it uses multiple decision trees inside it.
• It can easily handle large and complex datasets which have far more features.
• It can also easily tackle missing data problems, outliers’ detection, and noisy features.

Disadvantages of Random Forest Regression

• It can be not easy to understand.
• Subject matter experts may need to be involved for the Random Forest approach to be implemented successfully. They are essential for selecting and modifying parameters such as the number of decision trees, the maximum depth per tree, and the number of features to be taken into account at each split. A few key choices must be made to optimize the algorithm's performance and ensure accurate forecasts.
• Processing large datasets can be computationally costly.
• Overfitting can be a concern for Random Forest models when they become overly complex or contain an excessive number of decision trees. As a result, the model can perform poorly on fresh, untested data and overfit the training set.

Summary

Random Forest regression emerges as a robust solution for both continuous and classification prediction tasks, offering distinct advantages over traditional decision trees. Its ability to manage high-dimensional data, capture intricate relationships, and mitigate overfitting has propelled its widespread adoption across various domains and applications. Within a Random Forest, each constituent tree contributes its "vote" towards determining the most prevalent class in classification tasks or providing a prediction in regression scenarios. However, there's a risk of overfitting when employing excessively deep Random Forests or dealing with large and intricate datasets. Additionally, compared to individual decision trees, algorithms for Random Forest may exhibit lower interpretability due to their ensemble nature.

Python Code

here is the random forest Python example code: - 

DECISION TREE IN MACHINE LEARNING/PYTHON/ARTIFICIAL INTELLIGENCE

Decision Trees

• Decision Trees
• Advantages of Decision Trees
• Disadvantages of Decision Trees
• Appropriate Problems for Decision Tree Learning
• Practical Issues in Learning Decision Trees
• Classification and Regression Tree Algorithm 

One famous supervised ML technique that can handle ML classification and regression issues is random forest. It uses the combined knowledge of several classifiers to solve complex problems, based on ensemble learning concepts. This technique improves the learning system's overall performance by using the strengths of many models.

Random forest improves accuracy and reduces overfitting by combining predictions from several decision trees. There is intrinsic substantial diversity in individual decision trees. The resultant variation, however, is reduced when these trees are simultaneously integrated into a random forest. This decrease happens because training each decision tree on a different set of data ensures that the result is dependent on numerous trees rather than just one, which in turn reduces the overall variance.

Raising the tree count in a random forest model improves accuracy and strengthens the model by reducing the likelihood of overfitting.

Utilized in machine learning, decision trees facilitate the organization of judgment generation by reducing complicated situations to a set of alternatives derived from incoming data. Using these decision nodes, we were able to segment the information and forecast a target variable. Every node stands for a feature, and the branches indicate possible outcomes that are based on the feature's value. Because of their use in classification and regression issues as well as their ease of understanding and analysis, decision trees find widespread application in many domains, including healthcare, marketing, and finance they are also dynamic decision tree. With its straightforward structure and capacity to capture nonlinear relationships and interactions among parts, decision trees prove to be an invaluable tool for modeling intricate data patterns. Overfitting can occur if regularization or pruning is not done correctly, leading to models that are not as generalizable. However, by serving as the basis for more advanced ensemble approaches like as random forests and gradient boosting, decision trees significantly advance the areas of predictive modeling and data analysis. In this blog, we learn about decision tree machine learning Python. 

Why do we use Decision Trees?

The two main reasons why we should use decision trees are:

1. Decision trees aim to replicate human decision-making processes, making them straightforward to comprehend.
2. We can easily understand it because it looks like a tree structure.

Decision tree example

Let’s suppose Noah, a park ranger with a passion for wildlife, was tasked with a new challenge: the challenge is to identify the increasing number of mysterious animal tracks appearing on the trails. The methods previously used are not enough. Noah needed a smarter way to decipher these cryptic clues. Here enters the decision tree method, Noah's secret weapon for unraveling the tracks’ secrets.

Imagine a decision tree machine learning as a series of questions, like a branching path in the forest. Each question focuses on a specific characteristic of the tracks, like size, number of toes, and claw marks, depth, stride length. Noah fed these data and more related data into the decision tree, paired with the corresponding animal identified by experienced trackers.

The decision tree doesn’t just memorize facts, it learns by analyzing the data. It creates a series of yes-or-no questions based on the most relevant track features. For example, the first question might be: “Are the tracks larger than 6 inches?” depending on the answer, the decision tree would branch out, leading to further questions about the number of toes or claw marks.

This branching structure very much looks like a detective’s flowchart, narrowing down the possibilities with each answered question. The decision tree continues branching until it concludes – the most likely animal that left the tracks.

The decision tree algorithm real test comes when Noah encounters a set of fresh tracks unlike any he’d seen before and he carefully measures and observes them, and provides this information to the decision tree algorithm to predict the outcome or in this case the animal.

Noah started with the initial question: “Are the tracks larger than 6 inches?” Yes. The tree branched, leading to the next question: “Do the tracks have tree toes?” No. This eliminated possibilities like deer or rabbits. The tree continued branching, asking about claw marks, stride length, and other details.

Finally, after a series of questions, the decision tree concluded: and this conclusion is that the tracks are most likely left by a bobcat.

Decision tree terminologies

Key Terminologies in Building a Decision Tree:

Root NodeLocated at the top of the tree structure, the root node encompasses all the data, acting as the starting point for decision-making within the decision tree.

Decisions based on features added to the network are represented by these nodes. Internal nodes can give rise to more internal nodes or even leaf nodes as their offshoots.

A terminal node, also known as a leaf node, is a node that has no child nodes and is used to indicate a class name or a number value.

Splitting - divides a node into several smaller nodes according to a split criteria and a chosen attribute.

Beginning from an inner node and ending at a leaf node makes up the branch/sub-tree segment of the decision tree.

A node that splits to produce one or more child nodes is known as a parent node.

Parent Node: After passing through a split, this node gives birth to one or more offspring nodes.

Child Node: These nodes are created when a parent node is divided through a splitting process.

Impurity: It evaluates the consistency of the target variable in a subset of the data and indicates the randomness or uncertainty of the data set. Decision trees typically use additional metrics such as "entropy" and "Gini index" for classification tasks.

Variance: Variance in decision trees for regression problems illustrates the differences between the actual and predicted target variables in various dataset samples. This variance is typically measured using several metrics, such as mean square error, mean absolute error, Friedman's MSE, and half-Poisson Deviance.

Information Gain: This measure evaluates the uncertainty reduction achieved by partitioning the data tree according to a given feature of the decision tree. It evaluates the feature with the largest information gain for each node to determine which is the most informative feature to share, enabling the production of cleaner subsets.

Pruning: This entails deleting branches from the decision tree that do not give extra information that could potentially cause overfitting.

Attribute Selection Measures

Building a Decision Tree requires a learning phase where the initial dataset is partitioned into subsets using Attribute Selection Measures (ASM). ASM plays a crucial role in decision tree algorithms by evaluating the effectiveness of various attributes in dividing datasets. Its main goal is to identify attributes that result in the most similar-looking subsets after splitting so that the data benefit is maximized. This iterative process of recursive partitioning occurs for each subset or subtree, driving the gradual construction of the decision tree.

One notable aspect is that the construction of a decision tree classifier doesn't necessitate prior domain knowledge or specific parameter settings, making it valuable for exploratory knowledge discovery. Additionally, decision trees are adept at handling high-dimensional data.

Entropy serves as a metric to gauge the level of randomness or uncertainty within a dataset. In datasets designed for classification tasks, entropy serves as a measure of randomness, calculated according to the distribution of class labels present in the dataset.

For a given subset of the original dataset containing a K-class for the ith node, entropy serves as a metric of the level of disorder or uncertainty in that subset. This evaluation helps to evaluate the impurity of a node and influences the selection of attributes during the construction of the decision tree. 

In the above equation:

• S is the dataset sample.
• k is the particular class from K classes
• p(k) is the proportion of the data that belong to class k to the total number of data points in dataset sample S.

• In the equation p(i, k) must not equal to zero.

There are some important points we should remember if we are using entropy, and they are:

1. If the dataset is fully homogeneous then the entropy is 0, which means that every instance or data point in the dataset belongs ton a single class. It is the lowest entropy which indicates that there is no uncertainty in the dataset sample.
2. The entropy is the dataset's highest value if it is equally divided into several subclasses. This suggests that maximal uncertainty in the dataset sample is indicated by a uniform distribution of class labels, which is when entropy is highest.
3. Entropy is also utilized to evaluate the effectiveness of a split. It aims to create more homogeneous subsets within the dataset concerning class labels, thereby minimizing the entropy of the resulting subsets.
4. The decision tree algorithm selects the attribute with the highest information gain as the splitting criterion, and this process iterates to construct the decision tree further.

Gini Impurity or Index – The Gini index serves as a metric to assess the accuracy of a split among classified groups, providing scores between 0 and 1. A score of 0 indicates that all observations belong to a single class, while a score of 1 signifies a random distribution of elements across classes. Hence, aiming for a lower Gini index score is ideal, indicating more homogeneous subsets after the split. This metric serves as an evaluation tool for decision tree model, allowing the assessment of the model's effectiveness in creating informative splits.

In the above equation, p_i (p subscript i) is the proportion of elements in the set that belongs to the i^th category.

Information Gain – Information gain measures the decrease in entropy or variance achieved by dividing a dataset according to a specific attribute. Within decision tree methods or algorithms, it evaluates the importance of a feature by creating subsets that are more uniform or homogeneous concerning the class or target variable. A higher data gain suggests that the feature is more useful for predicting the target variable, highlighting its importance in improving the uniformity of subsets after splitting.

The information gain of an attribute A, concerning a dataset S, is calculated as follows:

In the above equation

• A is the specific attribute or class label
• |H| is the entropy of dataset sample S
• |H_V | (H subscript V) is the number of instances in the subset S that have the value v for attributes A.

When building a decision tree, the primary criterion for partitioning is the most informative attribute. The amount of entropy or variation that is reduced when a data collection is divided based on attribute A is indicated by data validation.

Data gathering is essential to the operation of regression and classification decision trees. Regression trees consider variance, while classification trees consider entropy when evaluating additiveness. In their information gain computations, both types use variance or entropy, which is the same regardless of the impurity measurements used.

Decision tree algorithm and its working

The decision tree analyzes the dataset to generate predictions regarding its categorization. It looks at the base node of the tree first. The record's attribute has now been compared by the algorithm to the values of the dataset's root attribute. Afterward, it proceeds down the branches using this comparison, verifying the specific attribute requirements met at every stage to decide whether to go on to the next node or not.

Iteratively analyzing and comparing the dataset or datasets is done at each subsequent node of the decision tree. Until it reaches the leaf node of the tree, the cycle keeps going. You will be able to comprehend the full operation by following these steps.

First Step: The dataset is contained in the root node, S, at the very top of the tree.

Second Step: The algorithm's second phase involves determining which attribute in the dataset is the best one using the Attribute Selection Measure (ASM).

Third Step: The algorithm splits dataset S into subsets containing possible values for the best attribute in the third stage.

Fourth Step: Making a decision tree node with the selected top attribute is the fourth step.

Fifth Step: The process iteratively creates new decision trees using the dataset subsets created in Step 3. The recursive construction process ends and the final node in the categorization or regression tree process is designated as a leaf node when more classification is no longer possible.

Let’s suppose there is a man who wants to buy a new mobile phone now he needs to decide which type of mobile phone he wants to solve this problem we can use a decision tree and it starts with the operating system the use want after that we can split the node into parts which also have decision node, then if the user selects the operating system then it move to next node which is related to camera quality and then it split into different nodes (it also has decision node) after selecting the node it split into processor name and brand and then split into further category this process continues till we goes to leaf node (every node has its own decision node). By using this mechanism we can easily select or decide which mobile phone users want.

further category this process continues till we go to the leaf node (every node has its own decision node). By using this mechanism we can easily select or decide which mobile phone users want. The above decision tree chart shows the working of the decision tree.

Advantages of the Decision Tree

• It is easy to understand because it also tries to follow the same process that human follows to make a decision.
• It is very useful in cases where we need to make a decision.
• It can help to think about what may also be the possible outcome of the problem and also what are the other results.
• For this algorithm we need to clean the data less as compared to other algorithms.

Disadvantages of the Decision Tree

• As the decision tree grows, it develops several layers, and when the data is very heterogeneous, the tree expands with additional layers, increasing complexity.
• Another issue related to decision handicaps is potential overfitting, which occurs when a model picks up noise in the training data instead of general patterns. One way to solve this problem is to use the Random Forest algorithm.
• As the number of class identifiers increases, the computational complexity of the decision tree can also increase.

Appropriate problems for Decision tree learning

Decision tree learning is particularly well-suited for problems characterized by the following traits:

Instances represented by attribute-value pairs: Commonly, instances are portrayed using attribute-value pairs, such as temperature, and corresponding values like hot, cold, or mild. Ideally, attributes possess a finite set of distinct values, simplifying the construction of decision trees. Advanced versions of decision tree algorithms can handle attributes with continuous numerical values, allowing the representation of variables like temperature on a numerical scale.

Discrete output values for the target function: Decision trees are commonly developed for categorical Boolean examples, like binary outcomes like yes or no. While primarily used for dual outcomes, decision tree methods can be developed to handle functions with multiple distinct output values, albeit applications with numeric outputs are less frequent.

Need for disjunctive descriptions: Decision trees naturally accommodate disjunctive expressions, enabling effective representation of complex relationships within data.

Resilience towards errors in training data: Decision tree learning techniques demonstrate resilience towards errors present in training data, including inconsistencies in categorization or discrepancies in feature details characterizing cases.

Handling missing attribute values in training data: In some cases, training data might have missing or absent characteristics. Despite encountering unknown features in certain training samples, decision tree approaches can still be applied effectively. For example, when considering humidity levels throughout the day, this data might be available for only a specific subset of training samples.

Practical issues in learning decision trees include

• Selecting the decision tree's growth depth 
• Taking care of the enduring traits
• Choosing the Most Effective Attribute Selection Metric
• Interpreting training data in the presence of blank values for attributes
• Handling attributes with different price points.
• Increasing the efficiency of computing

The CART (Classification and Regression Trees) algorithm is utilized to create decision trees for both classification and regression purposes. It functions akin to techniques such as Gini impurity or Information Gain, prioritizing the selection of the optimal split at each node according to a designated metric. The basic procedure of the CART algorithm can be outlined as follows:

1. Root Node Initialization: Begin with the root node of the tree, representing the complete training dataset.
2. Evaluate Feature Impurity: Examine all features in the dataset to measure the level of impurity in the data. Classification tasks use different metrics to quantify impurities, such as the Gini index and entropy, while regression tasks use metrics such as root mean square error, mean absolute error, friedman_mse, or semi-Poisson deviation.
3. Select the Feature That Will Provide the Most Information: Determine whether the data-splitting feature minimizes impurity or produces the most relevant information.
4. Partition the Database: Divide the data set into two groups, one for each possible attribute value that has been selected; for instance, "yes" and "no" for each conceivable attribute value. The goal is to create subsets that are as comparable to the dependent variable as possible.
5. Assess Subset Impurity: Evaluate the impurity of each resulting subset based on the target variable.
6. Iterative Process: Continuously repeat steps 2-5 for each subset until the termination condition is met. The stopping criteria can reach the maximum tree depth, reach how many minimum samples are needed to split or reach the minimum limit of impurities.
7. Assign Values to Terminal Nodes: For each terminal node, also known as a leaf node, determine the most frequent class in the tree for classification tasks or the average for regression tasks. This task ensures that the model can make predictions for new data instances using the models learned during training.

These steps collectively facilitate the creation of an effective decision tree through iterative evaluation and feature splitting, catering to both classification and regression tasks.

Classification and Regression Tree algorithm for Classification

Let’s have data at node m be Q_m (Q subscript m) and it has n_m (n subscript m) samples, and t_m (t subscript m) as the threshold for the node m. After that, we can write the classification method using a regression tree:

In the above equation

• The impurity measure for the left and right subsets at node m is denoted as H. H's value can be determined by analyzing entropy or Gini impurities.
• n_m (n subscript m) is the total number of instances encountered at node m during the left and right sunsets.

For selecting the parameter, we can write the equation as:

Classification and Regression Tree algorithm for Regression

For Regression Problems We can do the following to get the equation, let the data available at node m be Q_m and it has n_m samples and t_m as the threshold for them. Then, the classification and regression algorithm for regression can be written as:

In the above equation, the MSE is the mean squared error.

n_m (n subscript m) is the number of instances in the left and right subsets at the node m.

For selecting the parameter, we can write the equation as:

Strengths of the Decision Tree Approach

• Decision trees offer interpretability due to their rule-based nature, allowing easy comprehension of generated rules.
• They perform classification tasks with minimal computational demand.
• Capable of handling both continuous and categorical variables, making them versatile.
• Highlighting the importance of fields for prediction or classification purposes.
• Simplicity in usage and implementation, requiring no specialized expertise, rendering them accessible to a broad user base.
• Scalability we can scale the Decision tree to very long.
• The ability to flexibly handle missing data or values makes them well-suited for incomplete or missing data sets.
• Ability to handle non-linear relationships between variables, enhancing suitability for complex datasets.
• Adequacy in handling imbalanced datasets by adjusting node importance based on class distribution, ensuring functionality even with heavily skewed class representation.

Weaknesses of the Decision Tree Approach

• When it comes to predicting continuous attribute values, decision trees tend to be less efficient for estimation tasks.
• In classification scenarios with numerous classes and a limited training dataset, decision trees often encounter challenges, resulting in higher error rates.
• The computational expense in training decision trees can be notable, particularly when growing trees to fit larger datasets. Sorting candidate splits and searching for optimal combinations of fields during tree construction can be resource-intensive. Similarly, pruning algorithms can be costly as they involve forming and comparing numerous sub-trees.
• There's a high risk of overfitting, particularly with complex and deep trees, impacting the performance on new, not-seen data.
• Little variations in training data might yield entirely new or separated decision trees, complicating result comparison or reproduction.
• Many decision tree models encounter difficulties when dealing with missing data, requiring strategies such as imputation or deletion of records containing missing values to address this issue.
• Biased trees may result from improper initial data splitting, particularly in cases of unbalanced datasets or rare classes, which can affect the accuracy of the model.
• The scaling of input features can significantly impact decision trees, particularly when employing distance-based metrics or comparison-intensive decision rules.
• Decision trees have limitations in representing intricate relationships between variables, especially concerning nonlinear or interactive effects. This can lead to less accurate modeling of complex relationships within the dataset.

Summary

Among the most practical supervised learning models are decision trees for both regression and classification. They can separate data based on features by using a tree structure, where nodes represent features, branches represent decisions, and leaf nodes represent outcomes or forecasts.  Known for their interpretation and visualization capabilities, decision trees contain various types of data, including numerical and categorical data. However, they run the risk of overfitting without proper pruning and may not capture complex data relationships as effectively as alternative algorithms. Using techniques such as ensemble methods can improve their efficiency and robustness.

Python Code