Classification and Prediction

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Formal Classification and prediction Definition: 

  • Classification and prediction are two forms of data analysis those can be used to extract models describing important data classes or to predict future data trends.
  • Such analysis can help to provide us with a better understanding of the data at large.
  • Classification predicts categorical (discrete, unordered) labels, prediction models continuous valued functions.

Let’s Understand Classification a morsel more:

  • The goal of data classification is to organize and categorize data in distinct classes.
  • A model is first created based on the data distribution.
  • The model is then used to classify new data.
  • Given the model, a class can be predicted for new data.
  • In general way of saying classification is  for discrete and nominal values.

Let’s Understand Prediction a morsel more:

  • The goal of prediction is to forecast or deduce the value of an attribute based on values of other attributes.
  • A model is first created based on the data distribution.
  • The model is then used to predict future or unknown values.

Summarization  of Classification and Prediction:

  • If forecasting discrete value ( Classification )
  • If forecasting continuous value ( Prediction )

Understanding Classification and prediction  in DataAspirant way:

Movies-Cover-Pic

 

Classification:

  • Suppose from your past data ( train data ) you come to know that your best friend likes above movies.
  • Now one new movie ( test data ) released and hopefully you want to know your best friend like it or not.
  • If you strongly conformed about chances of liking that movie by your friend, you can take your friend to movie this weekend.
  • If you clearly observe the problem it is just whether your friend like or not.
  • Finding solution to this type of problem is called as classification. This is because we are classifying the things to their belongings (yes or no, like or dislike )
  • Keep in mind here we are  forecasting discrete value( classification ) and the other thing this classification belongs to Supervised learning.
  • This is because you are learning this from your train data.
  • Mostly classification is binary classification in which we have to predict whether output belongs to class 1 or class 2 (class 1 : yes, class 2: no )
  • We can use classification for predicting more classes too. Like (suppose colors: RED,GREEN,BLUE,YELLOW,ORANGE)

Prediction:

  • Suppose from your past data ( train data ) you come to know that your best friend liked above movies and you also know how many times each particular movie seen by your friend.
  • Now one new movie ( test data ) released same like above, now your are going to find how many times this present newly released movie will your friend watch is it , 5 times, 6 times,10 times anything.
  • If you clearly observe the problem it is about finding the count, some times we can say this as predicting the value.
  • Keep in mind, here we are forecasting continuous value ( Prediction ) and the other thing this prediction is also belongs to Supervised learning.
  • This is because you are learning this from you train data.

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