Machine Learning Project - Iris Flower Classification | @dsbrain

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[@nallachi2913]

Nice content 👍

[@dsbrain]

Thank you 🙏

[@ghostlyverse2722]

❤❤❤❤ awsome bro 😊😊

[@dsbrain]

Thank you bro 🙏. Comment down suggestions to improve

[@ghostlyverse2722]

Already doing best my brother

[@dsbrain]

@@ghostlyverse2722 i believe there's improvement in everything. And im new to this from of teaching. Support us 💪🙏

[@ghostlyverse2722]

All ways will bro

[@sanjanahalli1174]

Can i run this code in vscode?

[@Saiii69]

Yes u can!!

[@QAYNATSHAMA]

❤❤❤❤❤

[@dsbrain]

Thank you❤️

[@sin3divcx]

But accuracy = 1 may be due to overfitting..

[@dsbrain]

Absolutely. This was just simple project. I would be glad to hear from you further after doing more exploration with this

[@tradingtigers6134]

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

[@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 = KNeighborsClassifier(n_neighbors=n)
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.

[@sravanihoney-b4f]

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