There are many real-life use cases to create your unique machine learning projects. If you’re still struggling to work on an actual use case, find something practical and unique, like a machine learning project where you’ll show a comparison of some of the classification algorithms in machine learning. So, if you want to know how to compare classification algorithms, this article is for you. In this article, I will present a comparison of classification algorithms in machine learning using Python.
Comparison of Classification Algorithms
In machine learning, classification means training a model to specify which category an entry belongs to. There are so many classification algorithms in machine learning, so if you can show a detailed comparison of classification algorithms in machine learning, it will become an amazing and unique machine learning project as a beginner. For this task, you must first choose a classification-based problem statement and determine all those classification algorithms that may be useful for your problem. Next, you need to train classification models and show a comparison based on their performance.
The performance of all classification algorithms will depend on the problem you are working on. So let’s start this task by importing the necessary Python libraries, a dataset based on the problem of classification, and some of the popular classification algorithms:
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The dataset I’m using here is based on social media marketing, I won’t analyze this dataset at this time, but when building your project, you need to show a detailed exploration of your data. You can find a detailed analysis of this dataset here.
Now let’s move forward to the task of comparing the performance of classification algorithms in machine learning. Here you can either choose only one performance evaluation metric or more, but the process will remain the same as shown in the code below:
In the above code:
- I first divided the data into training and test sets;
- Then I stored all the classification algorithms provided by the scikit-learn library in Python in their respective variables;
- Then I used the fit method to fit the data in the algorithm;
- Finally, I created a DataFrame, where I stored the model score on the data.
Below is the DataFrame you will see at the end:
|Decision Tree Classifier||0.9800|
|Passive Aggressive Classifier||0.6425|
According to the above output, the Decision Tree classification algorithm performs the best on this dataset.
So this is how you can compare classification algorithms in machine learning using the Python programming language. If you follow all the steps mentioned in this article while further exploring your dataset, it will become an amazing machine learning project as a beginner. Hope you liked this article on a comparison of classification algorithms in machine learning. Please feel free to ask your valuable questions in the comments section below.