Concepts in Machine Learning-CST 383 KTU-Minor Notes-Dr Binu V P

  Classification is one of the most used forms of prediction where the goal is to predict the class of the record. For binary classification, we aim to predict whether a record is a 1 or a 0 such as spam/not spam or churn/not churn and for multiclass classification, we aim to predict the class of a record such as classifying a mail as primary/social/promotional, etc. In addition to predicting the class of our records most of the time, we also want to know the predicted probability of belonging to the class of interest. These probability values are also called propensity scores. We can set a cutoff probability for the class of interest and for the records having propensity score below we consider the record belonging to that class, and vice versa. For classification problem that is a form of supervised learning we start with a labeled data where the class of the records is known, we train our model using this data, and then apply the model to the new data where the class is unknown. I

What is Clustering? Clustering is the process of making a group of abstract objects into classes of similar objects. Points to Remember A cluster of data objects can be treated as one group. While doing cluster analysis, we first partition the set of data into groups based on data similarity and then assign the labels to the groups. The main advantage of clustering over classification is that, it is adaptable to changes and helps single out useful features that distinguish different groups. Applications of Cluster Analysis Clustering analysis is broadly used in many applications such as market research, pattern recognition, data analysis, and image processing. Clustering can also help marketers discover distinct groups in their customer base. And they can characterize their customer groups based on the purchasing patterns.(customer segmentation) In the field of biology, it can be used to derive plant and animal taxonomies, categorize genes with similar functionalities and gain insigh