In Machine Learning, a Decision Tree is one of the most beginner-friendly algorithms that helps in Binary Classifications. But “Tackling Multiclass Classification with Complex Decision Tree” is not that easy.
In Binary Classification, where only two choices are present, in Multiclass Classification, we have to predict from multiple choices, which makes the dataset more complex to handle by the Decision Tree.
Still, if you face difficulties in understanding these, you may not know the proper ways or strategies for it. In such situations, one can seek assistance from CodingZap experts for Machine Learning assignment help.
In this article, we will first briefly discuss the Decision Tree and Multiclass Classification. Later, we will tell some useful strategies to easily tackle Multiclass Classification with a Complex Decision Tree. So, let’s start.
TL;DR: Multiclass Classification With Complex Decision Tree
Aspect | Summary |
Decision Tree | An ML algorithm that has a flowchart-like structure that splits data using Yes or No questions. It uses nodes and leaves to classify data into outcomes. |
Reasons for Complexity | Too many features, multiple class labels, class imbalance, and large datasets increase tree depth and branches. |
Multiclass Classification | A classification task where there are three or more possible outcome classes, like Predicting Fruit Types. |
Key Strategies |
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Real-world Applications | Healthcare (disease prediction), Banking (loan risk), Text filtering (spam/promotions), Image recognition, Retail (customer segmentation). |
What Are Some Strategies To Tackle Multiclass Classification With Decision Trees?
Tackling Multiclass Classification with a Complex Decision Tree is not easy, as has been proven from the above discussions. But, to make the handling easy, we have to use some useful strategies.
Here, we will show such 5 strategies that will make handling multiclass classification easy with decision trees.
Strategy 1: Limit The Depth Of The Tree
When we are building a decision tree, it tries to split the data as much as possible to make all the leaves pure, which causes a deeper tree with many branches that will focus on tiny details. This creates Overfitting issues.
So, when we limit the Depth of the Tree, it will only focus on the most important decisions. This will make the tree shorter, but it will learn the Big Patterns. There are some tips you can follow to complete this strategy.
- We should always use the Max_Depth parameter to limit the depth of the tree.
- One should start with a very small depth, like 3 or 5. Later, we can increase them.
- You should not create a very deep tree unless the dataset is very large.
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Strategy 2: Use Minimum Samples Per Leaf Or Split
A Decision Tree splits the classes until it makes all the classes separate. But to do so, it splits fewer classes as well. Those can be easily calculated if they are still in groups. This again causes Overfitting issues.
To remove such problems, you have to limit the Minimum Number of samples per leaf or split. This will not cause many branches in the tree, and it will only focus on the Meaningful Groups. Let us see some tips for it.
- You can use the Min_Samples_Split parameter to limit the splitting of the samples.
- The Min_Samples_Leaf parameter can be used to give a threshold value to each leaf to carry data.
- First, start with small values like 2 or 5. And then, adjust them as per your needs.
Strategy 3: Select Only The Necessary Feature
Every dataset comes up with different types of features or information. Not every feature is important to predict the result that you are looking for. In such cases, remove the features that are not needed.
If you are predicting the fruits dataset, then the ‘Color’ feature might be useful. But the ‘Value’ feature is not that useful. So, don’t use such features; this will make the tree deeper and complex for multiclass.
- You should check the feature importance score from the tree and note it.
- Remove the features that have less importance and do not help in the classification process.
- You can use the Domain Knowledge to drop the irrelevant features from the table.
Strategy 4: Use The Ensemble Methods
A single decision tree is both powerful and sensitive. If there is any small change, then the complete structure of the tree will get damaged. That is why there are some Ensemble Methods that merge multiple trees.
Random Forest and Gradient Boosting are some important Ensemble Methods in Machine Learning. Where Random Forest uses Multiple Decision Trees, the Gradient Boosting does Sequential Error Correction.
- To get the Balanced Performance, the Random Forest Method should be used.
- We can increase the number of trees using the N_Estimators parameter for better stability.
- To check harder tasks with small errors, the Gradient Boosting Method can be used.
Strategy 5: Work On The Imbalanced Classes
In Multiclass Classification, there might be one class that has more samples than the other classes. In such cases, the decision tree will tilt towards the class that has more samples and become biased.
If you are predicting Customer Feedback, and 80% ‘Positive’, and 20% ‘Negative’ samples are present, then the tree will lean towards the positive samples as it dominates and ignores the less important classes.
- We have to use the Class_Weight=”balanced” parameter to make the tree balanced.
- Before training the model, we have to check the class distribution for better understanding.
- We can use undersample larger classes when needed in the code to make it balanced.
Practical Example: Tackling Multiclass Classification With Complex Decision Trees
We hope that the strategies discussed above have been clear to you. If they are still not cleared, then in this section, we will make it clear through a practical example where Multiclass Classification will be handled.
In the following code, we will show how to use the strategies effectively to reduce the complexity.
# Import Necessary Packages
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
# Loading The Dataset
a, b = load_iris(return_X_y = True)
# We Will Limit The Tree Depth
zap = DecisionTreeClassifier(max_depth=3, random_state=42)
zap.fit(a, b)
tree = zap.predict(a)
print("Decision Tree:\n") # Printing The Result
print(classification_report(b, tree))
# We Will Minimum The Samples Per Split Or Leaf
zap2 = DecisionTreeClassifier(max_depth=3, min_samples_split=4, min_samples_leaf=2, random_state=42)
zap2.fit(a, b)
tree2 = zap2.predict(a)
print("\nDecision Tree With Minimum Samples Per Split:\n")
print(classification_report(b, tree2))
# The Ensemble Method- Random Forest Will Be Used
one = RandomForestClassifier(n_estimators=100, max_depth=3, random_state=42)
one.fit(a, b)
pred = one.predict(a)
print("\nRandom Forest:\n")
print(classification_report(b, pred))
Step 1: Importing The Libraries
- First, we will import the Iris Flower Dataset.
- Then, the DecisionTreeClassifier will be imported to build decision trees.
- While developing the Random Forest, the RandomForestClassifier will be used.
- To show the performance using Precision, Recall, and F1-score, the Classification_Report was imported.
Step 2: Loading The Dataset
- First, we will load the Iris Dataset into the program.
- The ‘A ‘will have all the features of flower measurements.
- ‘B’ will have the target labels like Flower Species in the program.
Step 3: Create A Decision Tree With Limited Depth
- A decision tree will be created with a Maximum Depth of 3.
- Then, we will train and predict it using Fit() and Predict() Functions.
Step 4: Create A Decision Tree With Minimum Samples
- Another decision tree will be created, which will also have a Maximum Depth of 3 and other restrictions.
- The Minimum Sample per Split will be 4, and the Minimum Sample per Leaf will be 2.
Step 5: Applying The Random Forest
- The Random Forest will be created by combining 100 Decision Trees with a Maximum Depth of 3.
- We will train the data, predict, and print the report on the output.
Output:
What Are Some Real-World Applications Of Multiclass Classification With Complex Decision Trees?
The Multiclass Classification with Complex Decision Tree has a large list of real-world applications. This problem is developed to solve many real-world problems. In this section, we are going to see some of them.
Let us check the following list of real-world applications before ending our discussion.
- The Healthcare Section, the Multiclass Classification is highly used to diagnose the disease. A model can check for similar symptoms and predict the disease into Flu, Pneumonia, Corona, etc.
- In the Banking sector, the Loan Applications are categorized using the Multiclass Classification. It can be classified into Low Risk, Medium Risk, and High Risk to reduce fraud.
- Multiclass Classification can also be seen in Text Filtering. In Email Filtering, the text can be divided into Promotions, Updates, Spam, etc., which are very useful.
- In the field of Image Recognition, the use of Multiclass Classification and Random Forest is high. Using them, an image can be classified into many objects, like a Cat, a Dog, a Car, etc.
- For the Retail and E-Commerce field, the Multiclass Classification is a big player. It can classify the customer like First Buyer, Returning Buyer, etc.
Conclusion
- A Decision Tree is an algorithm that has a flowchart-like structure, which helps in classification.
- Decision Tree is a beginner-friendly algorithm, but it gets complicated with Multiclass Classification.
- There are 5 useful strategies to make multiclass classification easy with a decision tree.
- Limiting the Tree Depth, using Minimal Samples per split, Balancing the Tree, etc., are important.
- From Healthcare, Banking, to Image Recognition, the use of this duo is everywhere.
