What is Feature Scaling? Explain the different feature scaling techniques
Feature scaling is a data preprocessing technique that involves transforming the numerical values of features to a standardized scale.
Feature scaling is a data preprocessing technique that involves transforming the numerical values of features to a standardized scale.
In the context of machine learning, “noise” in a dataset refers to the presence of random variation in the data that does not reflect the underlying relationship between the input features and the target variable.
Some of the popular use cases of the bag of words model are document similarity, text classification, feature generation, text clustering
Machine learning is a method of teaching computers to learn from data, without being explicitly programmed.
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Supervised learning refers to the task of learning from labeled training data in order to make predictions or classify new, unseen data.
In unsupervised learning the algorithms are not given any labeled data but instead the goal is to find patterns and relationships that are hidden in the input data.
Both discriminative and generative models have the ability to learn model parameters in a classification setting, but they are based on entirely different mechanisms and serve different purposes.
Linear models are a class of models in which a response variable is linearly related to one or more predictors.
Many non-linear relationships can be transformed into linear relationships through logarithmic and power transformations
Structured Data is data that has a clear and pre-defined schema. Unstructured Data encompasses the wide spectrum of data that does not fall within the structured category.
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