23 Jan

Types of ML problems

Classification

A classifier uses a set of instances for which the correct category membership is known.

Questions: spam or ham, positive or negative.

Training Data: ex. tweets which are correctly classified as positive or negative.

Regression 

Forecasting of continuous value.

Questions: what will be the price of this stock on a given date, what will be a sales of this product in a future week

Training Data: historical datapoints.

Clustering

Helps to identify the groups in raw dataset (groups of users in social network).

We’re telling how many groups there should be and algorithm makes grouping objects by different attributes into this number of groups.

Recommendations (Collaborative Filtering)

Determine what user may like based on past behavior.

 

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