Apply the K-Nearest Neighbors algorithm effectively to classify the renowned iris dataset, analyzing attributes such as sepal and petal dimensions. Understand crucial techniques and evaluation metrics to enhance predictive accuracy with machine learning.
Key Insights
- The article leverages the iris dataset, containing measurements on sepal length and width plus petal length and width, demonstrating the practical application of the K-Nearest Neighbors algorithm for classification.
- Essential Python libraries including NumPy, pandas, and sklearn's train-test split and K-Nearest Neighbors classifier are used for data preprocessing and model training.
- A classification report is employed to assess model performance, providing precision and recall metrics to evaluate the accuracy and effectiveness of the classification approach.
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We're going to now look at applying K&N, the K-Nearest Neighbors algorithm, to a more realistic dataset. We're going to use the famous iris dataset from sklearn. The iris dataset is a collection of iris flowers with their sepal length and width, and petal length and width.
And, you know, you don't need to know a lot about flowers to do this, fortunately. But we can plot these; we can feed the sepal length, sepal width, petal length, and petal width data to a K-Nearest Neighbors algorithm. And it will look at, hey, which one was closest among all four features to, you know, what are the nearest neighbors to that particular new flower.
And we'll find that this has surprisingly good accuracy. All right. So here are our imports.
Here are the things we'll need: NumPy and Pandas. We'll be showing you some images to help visualize this.
And we do need to load the iris data in. They give us a function called `load_iris` that we can use for that. We'll also have, you know, our more typical train test split and the K-Nearest Neighbors classifier model initialization.
And we'll also be using a classification report, which will show us precision, recall, and other useful evaluation metrics to see how we did. The other code we are giving you includes our Google Drive loading block. Let's run both of those.
This may take a minute if it's the first time running it, as it is for me. And we'll also want to grab Google Drive. So you'll run this block as well.
And once you've imported everything and loaded Google Drive, we'll dive into what these flowers are and what data we have to work with.