Machine Learning Decision Trees are an excellent tool for data scientists to make accurate predictions. If you haven't heard of them, you've landed in the correct port! We'll explore Decision Trees and how they work compared to popular Machine Learning techniques like KNN and SVM. In addition, we'll talk about the best algorithms you can use with decision trees. Are you ready to unfold all its branches?
What are Machine Learning Decision Trees?
Machine Learning Decision trees, AKA Classification and Regression Trees (CART), are a powerful predictive modeling technique. Data scientists and engineers can use them in supervised and unsupervised scenarios, including classification and regression.
A decision tree has a branching tree-like structure of nodes. Each internal node represents the split decisions of a given attribute or feature and predicts the value of a variable using decision rules inferred from the data features. The nodes represent different attributes in the dataset—each branch off towards the right or left based on those attributes' values. Machine learning decision trees optimize each split node's information gain. Thereby, they can effectively classify new data instances into different classes.
In simple terms, decision tree is a model that uses a tree-like structure to organize a collection of questions. These questions, also known as conditions or splits, are arranged hierarchically in the shape of a tree. In this class, we will refer to them as conditions. The nodes in the tree that are not leaves contain a condition, while the leaf nodes contain a prediction.
What Problems Do Decision Trees Solve?
Decision Trees are used to quickly build predictive models from large amounts of data to allow you to classify new data points according to existing labels. You can also predict unknown values associated with those labels via regression techniques. It makes them very useful in many fields, such as finance, healthcare, marketing, and advertising. In other words, they're excellent for areas where accurate predictions are crucial. They are best for analyzing customer behavior patterns or forecasting sales trends correctly.
Machine Learning Decision Trees’ Algorithms
The best algorithms for Machine Learning decision trees include C4.5 for a greedy heuristic to construct the tree and ID3 to create decision trees from a given dataset. Here, we can also see CHAID (Chi-squared Automatic Interaction Detector), using decision trees to identify relationships between different variables in a dataset, and C5.0 for boosting and pruning techniques. While Random Forest combines multiple decision trees to create an accurate predictive model, XGBoost (eXtreme Gradient Boosting) for supervised learning, specifically for classification and regression problems.
They all bring unique advantages to the training time and accuracy depending on the size of the dataset. For example, C4.5 is better suited for datasets with discrete features, such as binary classification tasks. On the other hand, C5.0 is for data mining and predictive analytics, and Random Forest is better for datasets with continuous features like regression tasks.
Machine Learning Decision Trees' Advantages
One of the primary advantages of decision tree algorithms is that they provide an incredibly intuitive way to visualize data by displaying the relation between different variables. This allows the quick and easily create of models for predicting outcomes based on input variables. That's especially useful when dealing with large datasets that contain many features.
Moreover, decision trees have a high degree of accuracy. They're suitable for predictive analytics as well as classification tasks. You can also tune algorithms according to user preferences and requirements for maximum accuracy. Some other significant advantages include ease of interpretation, ability to handle numerical and categorical data, and relative speed compared to different Machine Learning algorithms.
Machine Learning Decision Trees' Disadvantages
Decision trees also come with some drawbacks which you should consider. Some drawbacks enclose them being prone to overfitting training data, becoming increasingly complex as they learn more data, and being susceptible to outliers or minor data changes.
Models can become overly specific. Thus, they may need help generalizing on unseen data samples outside their training set. You can use special techniques to avoid this issue, such as pruning or regularization. This way, you control complexity and ensure the model remains accurate on new datasets. Additionally, these models may not always find completely optimal solution paths, and it's due to their greedy nature of searching for locally optimal solutions instead of globally optimal ones.
Machine Learning Data Classification Methods
K Nearest Neighbor (KNN) Algorithm
K Nearest Neighbor (KNN) is a Machine Learning algorithm based on the distance between two points. KNN works by finding the nearest K data points to a given point in a dataset and then classifying it based on those data points.
KNN is simple to use in classification problems as it requires no assumptions on the distribution of the data or any prior knowledge of it. This approach also helps identify an object's category by looking at its closest neighbors. It has been used extensively in various applications such as image recognition, Natural Language Processing, and Recommender systems. KNN is also highly efficient when dealing with complex high-dimensional datasets.
Support Vector Machine (SVM) Algorithm
SVMs are Machine Learning algorithms that use a hyperplane to separate data points into various classes. This hyperplane can be a line, plane, or higher-dimensional subspace, depending on the number of features in the data set. An SVM aims to find the optimal hyperplane that maximizes the margin between multiple classes to ensure maximum accuracy.
These are pretty helpful with classification problems with two possible outcomes, such as spam filtering or facial recognition. Also, they are crucial in regression tasks and outlier detection.
Decision Trees vs. KNN and SVM
When compared to KNN and SVM, decision trees have some distinct advantages. They provide a visual representation of data and are more interpretable than KNN or SVM models, making it easier to understand the logic behind any predictions made by the model.
KNN and SVM algorithms require feature scaling and preprocessing like data normalization. Conversely, decision trees require very little preparation of data or no preparation at all. That gives decision trees an advantage over these models in terms of speed. Decision trees can handle complexities better than KNN and SVM when dealing with large datasets.
However, decision tree models may be prone to overfitting if many features are in use, which can also happen if there needs to be more depth in the tree structure. Moreover, they might not be suitable for particular data types, such as non-linear data.
Creating a Machine Learning Decision Tree in Python
When creating a decision tree using Python, the first step is to choose the tools you'll use to build it and gather the data. The most common Python tools used are Pandas, Numpy, and Scikit. Scikit even offers you a collection of datasets you can import if you need more data to make your decision tree. This is what your code should look like:
import pandas as pd
from sklearn.datasets import load_iris
data = load_iris() |
If you're using data from somewhere else, you might need to preprocess it a bit. You may have to remove any redundant features or values that could be causing bias in the model's performance. Then, the data is ready for training purposes. You should still take advantage of splitting their data into train/test sets. About 75% of the data will be used for training, while 25% for testing.
SciKit provides beneficial built-in functions for training and testing, with documentation for sklearn.model_selection.train_test_split() and sklearn.tree.DecisionTreeClassifier(). You'll have to pass the X and Y values and specify how much you'd like to set aside for testing. For example, test_size = 0.25, in this case, is 25%. To start making predictions, you can use two main methods. On the one hand, predict(x) predicts class labels for samples in X. On the other hand, predict_proba(x) gives probability estimates.
What is an Example of Machine Learning Decision Tree?
Let's put all this knowledge into a practical case! There is an eCommerce company that wants to decide whether or not to accept a customer's order for an item. It can use a decision tree to determine the answer by analyzing the users' past orders, browsing history, and current geographical location.
The decision tree would start by asking questions about the customer and their order. Have they ordered from us before? Are they located near one of our distribution centers? Is the item in stock? All these questions can then be broken down into binary answers (yes/no), directing the decision-making process.
Conclusion
These tree-based methods are fantastic and helpful tools for data scientists to make quick predictions. They have tremendous advantages, such as high speed, accuracy, and ease of use. Plus, they allow you to work with numerical and categorical data, requiring very little pre-work with the data. On the other hand, you can visualize and interpret the models of a decision t easily. All that makes them incredibly useful when working with large datasets.
You can start working with decision trees in the blink of an eye using the excellent tools provided by Python, including SciKit, Pandas, and NumPy. A large community of developers strongly supports them. Plus, they offer comprehensible documentation to get you started with ease.
