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What are the assumptions of machine learning?
It assumes that there is minimal or no multicollinearity among the independent variables. It usually requires a large sample size to predict properly. It assumes the observations to be independent of each other.
What are the conditions for over fitting and under fitting?
Finally, you learned about the terminology of generalization in machine learning of overfitting and underfitting: Overfitting: Good performance on the training data, poor generliazation to other data. Underfitting: Poor performance on the training data and poor generalization to other data.
Why are assumptions important in modeling?
Checking model assumptions is essential prior to building a model that will be used for prediction. If assumptions are not met, the model may inaccurately reflect the data and will likely result in inaccurate predictions.
What are the assumptions of SVM?
Thus, SVMs can be defined as linear classifiers under the following two assumptions: The margin should be as large as possible. The support vectors are the most useful data points because they are the ones most likely to be incorrectly classified.
What are assumptions in modeling?
Model Assumptions denotes the large collection of explicitly stated (or implicit premised), conventions, choices and other specifications on which any Risk Model is based. The suitability of those assumptions is a major factor behind the Model Risk associated with a given model.
What are some examples of repeated measures in machine learning?
A good example of repeated measures is longitudinal studies — tracking progress of a subject over years. There are no model assumptions to validate for SVM. For tree-based models such as Decision Trees, Random Forest & Gradient Boosting there are no model assumptions to validate.
What is an example in machine learning?
An example is a collection of features. Machine Learning is generally categorized into three types: Supervised Learning, Unsupervised Learning, Reinforcement learning In supervised learning the machine experiences the examples along with the labels or targets for each example.
What is supervised learning in machine learning?
In supervised learning the machine experiences the examples along with the labels or targets for each example. The labels in the data help the algorithm to correlate the features. Two of the most common supervised machine learning tasks are classification and regression. In classification problems the machine must learn to predict discrete values.
What is the importance of labels in machine learning?
The labels in the data help the algorithm to correlate the features. Two of the most common supervised machine learning tasks are classification and regression. In classification problems the machine must learn to predict discrete values. That is, the machine must predict the most probable category, class, or label for new examples.