Labeling strategies are super important when it comes to how well your supervised learning models work. Based on what I’ve seen, here are some key points to keep in mind:
Quality Over Quantity: It's important to have high-quality labels. If your data has labels that are wrong or inconsistent, then even a lot of data won’t help your model. It’s way better to have a smaller number of well-labeled examples than a huge pile of bad ones.
Labeling Granularity: The detail in your labels is important too. For example, if you’re identifying pictures of animals, you should be careful about how you label them. Saying "dog" instead of "golden retriever" can change how well the model learns. More specific labels can make it better but might need more data to do it right.
Balanced Classes: Make sure that each of your labels is balanced. If one label is much more common than the others, the model might do a bad job when trying to predict the less common ones. You can use methods like oversampling or undersampling to keep things balanced.
Validation Strategy: How you divide your data into training, validation, and test sets is really important. Stratified sampling makes sure that every label is included in each group. This helps it reflect what you would actually see in real life.
By combining these strategies wisely, you can really boost how well your model performs. So remember to take your time and make sure your labels and splits are done right!
Labeling strategies are super important when it comes to how well your supervised learning models work. Based on what I’ve seen, here are some key points to keep in mind:
Quality Over Quantity: It's important to have high-quality labels. If your data has labels that are wrong or inconsistent, then even a lot of data won’t help your model. It’s way better to have a smaller number of well-labeled examples than a huge pile of bad ones.
Labeling Granularity: The detail in your labels is important too. For example, if you’re identifying pictures of animals, you should be careful about how you label them. Saying "dog" instead of "golden retriever" can change how well the model learns. More specific labels can make it better but might need more data to do it right.
Balanced Classes: Make sure that each of your labels is balanced. If one label is much more common than the others, the model might do a bad job when trying to predict the less common ones. You can use methods like oversampling or undersampling to keep things balanced.
Validation Strategy: How you divide your data into training, validation, and test sets is really important. Stratified sampling makes sure that every label is included in each group. This helps it reflect what you would actually see in real life.
By combining these strategies wisely, you can really boost how well your model performs. So remember to take your time and make sure your labels and splits are done right!