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What is identifier in machine learning?
# Identifiers are names for variables, functions, modules and other objects.
What are features and labels in machine learning?
Briefly, feature is input; label is output. This applies to both classification and regression problems. A feature is one column of the data in your input set. For instance, if you’re trying to predict the type of pet someone will choose, your input features might include age, home region, family income, etc.
Is system identification just machine learning?
Machine learning has been applied to sequential data for a long time in the field of system identification. As deep learning grew under the late 00’s machine learning was again applied to se- quential data but from a new angle, not utilizing much of the knowledge from system identification.
What is a feature independent variable?
Independent variables (also referred to as Features) are the input for a process that is being analyzes. For example, in the below data set, the independent variables are the input of the purchasing process being analyzed.
What is feature selection in ML?
Feature Selection is the process where you automatically or manually select those features which contribute most to your prediction variable or output in which you are interested in. Having irrelevant features in your data can decrease the accuracy of the models and make your model learn based on irrelevant features.
Why are some features not used in machine learning?
In many situations using all the features available in a data set will not result in the most predictive model. Depending on the type of model being used, the size of the data set and various other factors, including excess features, can reduce model performance.
What are the goals of feature selection in machine learning?
Depending on the type of model being used, the size of the data set and various other factors, including excess features, can reduce model performance. There are three main goals to feature selection. Improve the accuracy with which the model is able to predict for new data. Reduce computational cost. Produce a more interpretable model.
What are the different types of machine learning models?
There are a variety of ways to categorize a machine learning model. A model can be classified as belonging to different categories like: generative models, discriminative models, parametric models, non-parametric models, tree-based models, non-tree-based models.
Which is better generative or discriminative machine learning?
Generative models are computationally expensive compared to discriminative models. Generative models are useful for unsupervised machine learning tasks. Generative models are impacted by the presence of outliers more than discriminative models. Discriminative models model the decision boundary for the dataset classes.