RAG Datasets
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Machine Learning, Data Science, Python, Support Vector Machine, Artificial Intelligence.
Overview An SVM is a supervised machine learning algorithm that uses non-probabilistic linear classification to classify data. The basic idea behind SVMs is to find a hyperplane that best separates the data points into different classes by maximizing the margin between the two classes. SVMs are | [
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Machine Learning, Data Science, Python, Support Vector Machine, Artificial Intelligence.
particularly useful when dealing with complex, high-dimensional datasets and can handle both linearly separable and non-linearly separable data through the use of kernel functions. SVMs have been successfully applied in various fields such as computer vision, natural language processing, and | [
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] |
Machine Learning, Data Science, Python, Support Vector Machine, Artificial Intelligence.
bioinformatics, and have been shown to have high accuracy and generalization capabilities. Understanding SVMs For this lesson, we’ll be using the iris dataset provided by sklearn. Below shows the code for loading the data set, its description, & a picture to understand the “petal” and “sepal” | [
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] |
Machine Learning, Data Science, Python, Support Vector Machine, Artificial Intelligence.
attributes it describes. import pandas as pd from sklearn.datasets import load_iris iris = load_iris() print(dir(iris)) print(iris.DESCR) The first thing we want to do is get all this data into a pandas dataframe to better understand what we’re working with. In the code below, we create a | [
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] |
Machine Learning, Data Science, Python, Support Vector Machine, Artificial Intelligence.
dataframe, add a column called flower name that applies a lambda function naming each row based on the value it has under the target column, and prints the first, second, & third datasets within the dataframe. We can see that the first 50 rows are for the setosa flower, the next 50 are for the | [
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Machine Learning, Data Science, Python, Support Vector Machine, Artificial Intelligence.
versicolor flower, and the last 50 rows are for the virginica flower. df = pd.DataFrame(data=iris.data, columns=iris.feature_names) df['target'] = iris.target df['flower_name'] =df.target.apply(lambda x: iris.target_names[x]) print(df[:50].head()) print(df[51:99].head()) print(df[100:].head()) | [
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] |
Machine Learning, Data Science, Python, Support Vector Machine, Artificial Intelligence.
Let’s graph our data to visualize it. I’m going to separate the data into 3 different dataframes, corresponding to each unique flower. import matplotlib.pyplot as plt setosa_df = df[:50] versicolor_df = df[50:100] virginica_df = df[100:] plt.xlabel('Sepal Length') plt.ylabel('Sepal Width') | [
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] |
Machine Learning, Data Science, Python, Support Vector Machine, Artificial Intelligence.
plt.scatter(setosa_df['sepal length (cm)'], setosa_df['sepal width (cm)'],color="green",marker='+') plt.scatter(versicolor_df['sepal length (cm)'], versicolor_df['sepal width (cm)'],color="blue",marker='.') You could imagine it’s possible to draw a line diagonally across the graph from the bottom | [
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] |
Machine Learning, Data Science, Python, Support Vector Machine, Artificial Intelligence.
left to the top right, separating the setosa and versicolor flowers. This imaginary line (our classification boundary) is our support vector machine. There are many ways we could draw the line though, so how we do know what’s the optimal solution? The line with the highest margin, or distance | [
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Machine Learning, Data Science, Python, Support Vector Machine, Artificial Intelligence.
between the line and the corresponding data points, is the solution. In the graph above, we see each data point has a margin (drawn in red) between it and the classification boundary. We want a line that maximizes this distance. These data points near the line are referred to as “support vectors” | [
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Machine Learning, Data Science, Python, Support Vector Machine, Artificial Intelligence.
hence the name support vector machine. In the case we have only 2 features like the graph above, the boundary is a line. But in the case of 3 features, the boundary is a plane. Now imagine if there are more than 3 features… while it would be impossible to make an image that allows us to visualize | [
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