what is bais and variance. tell me assumption consider for different machine learning algorithm. explain architecture of CNN.

Question

Accepted Answer

Bias and variance are two sources of error in machine learning models. Different machine learning algorithms have different assumptions. CNN architecture consists of convolutional layers, pooling layers, and fully connected layers.

Bias is the error due to overly simplistic assumptions in the model, while variance is the error due to overly complex assumptions.
Different machine learning algorithms have different assumptions, such as linear regression assuming a linear relationship between features and target variable.
CNN architecture typically consists of convolutional layers for feature extraction, pooling layers for dimensionality reduction, and fully connected layers for classification.
Examples of machine learning algorithms with assumptions include Naive Bayes assuming independence between features, and decision trees assuming hierarchical relationships between features.

what is bais and variance. tell me assumption consider for different machine learning algorithm. explain architecture of CNN.

Popular interview questions of Data Scientist

Top HR questions asked in NexusLink Services Data Scientist