Machine Learning Project - Iris Flower Classification | @dsbrain

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[tradingtigers]

Bro I want this bro project bro by two days I want to submit mini project bro please helpe bro

[sindivcx]

But accuracy = 1 may be due to overfitting..

[dsbrain]

Im correcting a mistake i made in the video here! The n_neighbors are not selected based on the number of classes.

Here are a few considerations:

Odd vs. Even:

For binary classification problems, it's often recommended to use an odd number for n_neighbors to avoid ties.
For multiclass classification, you might want to choose a value that doesn't result in ties as well.

Rule of Thumb:

A common rule of thumb is to start with sqrt(N), where N is the number of data points. This can provide a good balance between overfitting and underfitting.
Cross-Validation:

Use cross-validation to evaluate different values of n_neighbors. This helps you assess how well the model generalizes to new, unseen data.
Plotting the performance metrics (e.g., accuracy, F1-score) against different values of n_neighbors can help you visualize the optimal choice.
Domain Knowledge:

Consider the nature of your data. If there are clear patterns or structures, you might choose a smaller n_neighbors. If the data is noisy or has a lot of outliers, a larger n_neighbors might be more robust.
Experimentation:

Try different values and see how they perform. You can use a loop to iterate over a range of values and evaluate the model's performance on a validation set.
For example, in Python:

for n in range(1, 21): # Try n_neighbors from 1 to 20
knn =
scores = cross_val_score(knn, X_train, y_train, cv=5, scoring='accuracy')
print(f'n_neighbors={n}, Mean Accuracy: {scores.mean()}')

Remember, there's no one-size-fits-all answer. It's often a balance, and the best value may depend on the specific characteristics of your dataset.