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What distinguishes a decision tree from a random forest?

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If you are interested in machine learning, you have probably heard of decision trees and random forests. These are two popular methods for supervised learning, where you want to predict a target variable based on some input features. But what are the main differences between them, and how do they affect their performance and applications? In this article, we will explain the basic concepts of decision trees and random forests, and compare their advantages and disadvantages.

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Mehul Sachdeva

Lead SDE @ Bank of New York | CSE, BITS Pilani | MITACS GRI 2022 | Apache Iceberg, Contributor | Dremio | Samsungâ€¦

1 Decision trees

A decision tree is a graphical representation of a series of rules that split the data into different groups based on some criteria. For example, if you want to classify an animal based on its characteristics, you might start by asking if it has feathers. If yes, then it is a bird. If no, then you might ask if it has fur. If yes, then it is a mammal. If no, then you might ask if it has scales. And so on, until you reach a final category. Each node in the tree represents a question or a decision, and each branch represents a possible answer or outcome. The leaf nodes at the end of the tree are the predicted classes or values.

To build a decision tree, you need to choose the best features and thresholds to split the data at each node. This is usually done by measuring how much each split reduces the impurity or uncertainty of the data. There are different metrics to quantify this, such as entropy, gini index, or mean squared error. The goal is to find the splits that create the most homogeneous and distinct groups, while minimizing the complexity and depth of the tree.

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Tim Davies

CEO
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They are random based on nutrition sunlight etc and they follow the normal laws of nature. Where as a decision tree has an objective who outcome can vary. Itâ€™s depend any on the singular event that proceeds it and it has boundaries ( for the most part ) . The problem with a decision tree is that it is more of a question tree with the decision being an output . Carefull attention needs to be paired to see if and universal scaling numbers are present or patterns . Ie the number 137 showing up or a seemingly fractal pattern that might elude the observer sure to scaling issues. Random things may only seem to be at the scale there observed. Eh one random set is quantified then added to another quantified random set andthere is emergent structure

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2 Random forests

A random forest is an ensemble method that combines multiple decision trees to create a more robust and accurate model. The idea is to use a technique called bagging, which stands for bootstrap aggregating. This means that you create many subsets of the data by sampling with replacement, and train a different decision tree on each subset. Then, you aggregate the predictions of all the trees by taking the majority vote for classification, or the average for regression.

By using bagging, you reduce the variance and overfitting of the individual trees, since they are less sensitive to the noise and outliers in the data. You also introduce more diversity and randomness in the model, since each tree sees only a fraction of the data and the features. This helps to avoid bias and improve generalization.

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3 Advantages of decision trees

Decision trees offer several advantages that make them desirable for machine learning. They are straightforward to comprehend and interpret, as they reflect human reasoning and logic. They are also versatile, as they can manage numerical and categorical features, as well as missing values and outliers. Furthermore, decision trees are speedy and scalable, since they only require basic calculations and comparisons at each node. Moreover, they can capture non-linear and complex relationships in the data, since they can create branches and splits in any direction.

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Mehul Sachdeva

Lead SDE @ Bank of New York | CSE, BITS Pilani | MITACS GRI 2022 | Apache Iceberg, Contributor | Dremio | Samsung Electronics
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A decision tree is a standalone model, whereas a random forest is an ensemble model comprising multiple decision trees. In a random forest, each tree is trained on a subset of the data, and the final prediction is based on the aggregation of predictions from individual trees. This ensemble approach enhances predictive accuracy and helps mitigate overfitting compared to a single decision tree.

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4 Disadvantages of decision trees

However, decision trees also have some drawbacks that limit their performance and applicability. For example, they can be prone to overfitting or underfitting due to their ability to grow too deep and complex or too shallow and simple. Additionally, decision trees are sensitive to small changes in the data since they create different splits and structures with different samples. Furthermore, they can suffer from high variance and low bias, fitting the data too well and generalizing poorly to new data. Lastly, decision trees can be unstable and inconsistent, producing different results with different runs and random seeds.

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Surya Pratap Singh Parmar

ML @ TikTok USDS | MS AI @ Northwestern University | Ex Samsung Research | IIT BHU
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To address overfitting in decision trees, the following strategies might help: 1. Maximum Depth: Maximum depth sets the maximum number of decision points from the root to the farthest leaf. By limiting this depth, you prevent the tree from growing excessively complex. This not only guards against overfitting but also enhances the tree's interpretability. 2. Minimum Samples for Split: Minimum Samples for Split sets a threshold for the minimum number of samples required to create a split at a node. This acts as a filter, ensuring that splits only occur when a node has enough data to make meaningful decisions rather than fitting to noise.

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5 Advantages of random forests

Random forests are advantageous over decision trees due to their accuracy and reliability, which is achieved by reducing the variance and overfitting of individual trees by taking the average of their predictions. Additionally, random forests are more robust and stable, as they reduce the sensitivity and instability of individual trees by using different subsets of data and features. Furthermore, they can handle large and high-dimensional data, as they are able to select the most relevant and informative features at each node. Finally, random forests are able to deal with class imbalance and noise, as they create more balanced and diverse groups through sampling with replacement.

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6 Disadvantages of random forests

However, random forests can be challenging for machine learning due to a few drawbacks. They are not as easy to comprehend or interpret, since they involve multiple trees and parameters. Additionally, they are slower and more computationally expensive compared to other methods, since they require more time and resources to train and test. Random forests can also suffer from high bias and low variance, since they may underfit the data and miss some important details. Furthermore, they can be overconfident and overestimate their performance, since they can ignore certain sources of error and uncertainty in the data.

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7 Hereâ€™s what else to consider

This is a space to share examples, stories, or insights that donâ€™t fit into any of the previous sections. What else would you like to add?

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Computer Science

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What distinguishes a decision tree from a random forest?

1

2

3

4

5

6

7

1 Decision trees

2 Random forests

3 Advantages of decision trees

4 Disadvantages of decision trees

5 Advantages of random forests

6 Disadvantages of random forests

7 Hereâ€™s what else to consider

Computer Science

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What distinguishes a decision tree from a random forest?

1

2

3

4

5

6

7

1 Decision trees

2 Random forests

3 Advantages of decision trees

4 Disadvantages of decision trees

5 Advantages of random forests

6 Disadvantages of random forests

7 Hereâ€™s what else to consider

Computer Science

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