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.
Nice content 👍
Thank you 🙏
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Thank you❤️
❤❤❤❤ awsome bro 😊😊
Thank you bro 🙏. Comment down suggestions to improve
Already doing best my brother
@@ghostlyverse2722 i believe there's improvement in everything. And im new to this from of teaching. Support us 💪🙏
All ways will bro
Can i run this code in vscode?
Yes u can!!
But accuracy = 1 may be due to overfitting..
Absolutely. This was just simple project. I would be glad to hear from you further after doing more exploration with this
Bro I want this bro project bro by two days I want to submit mini project bro please helpe bro
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.
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