লক্ষ্য করুন: এই এন্ট্রির অনুবাদ বর্তমানে মান পর্যালোচনার অধীনে রয়েছে, তাই কিছু বিষয়বস্তু সাময়িকভাবে শুধুমাত্র ইংরেজিতে প্রদর্শিত হচ্ছে।
এই এন্ট্রিটি এখনও আপনার ভাষায় অনুবাদ করা হয়নি, তাই নিচে মূল লেখাটি দেখানো হচ্ছে।
underfitting
This term is a technical descriptor used almost exclusively within the fields of data science, statistics, and machine learning. It describes a failure of a model to learn the training data, typically because the model is too rigid or simple. This is contrasted with overfitting, where a model learns the noise in the data rather than the signal.
When used as a noun, it refers to the state or phenomenon of the error. When used as an adjective, it describes the specific model or the nature of the error. Because it is a specialized technical term, it is rarely used in general conversation outside of a professional or academic context involving predictive modeling.
Meanings
Examples
The accuracy is terrible because we are dealing with severe underfitting.
I think the model is underfitting the training data.
Avoid underfitting by increasing the number of layers in your neural network.
Is this a case of overfitting or underfitting?
Is this a case of overfitting or underfitting?
The simple linear regression resulted in underfitting since the relationship was non-linear.
The simple linear regression resulted in underfitting since the relationship was non-linear.
We must reduce the regularization strength to stop the underfitting.
The model is too basic and is clearly underfitting.
Maybe I should add more features to prevent underfitting.