Learn how to train an ordinal regression model using technical and sales data to define price ranges, from low cost to very high cost, for a new mobile phone in the market.
Table of Contents
Question
Your company created a new mobile phone. You need to define a price range (0 – low cost to 3 – very high cost) for the phone. You collected technical and sales data for the phones on the market. Now you are ready to train your model. Here is your train dataset:
What type of model will you train?
Answer
Classification Model
Explanation
We are training the Classification model. In our case, we are using the historical data and predicting the price range category that a new phone belongs to.
The “Price range” column is our target or label, and it has four classes: 0 (low cost), 1(medium cost), 2 (high cost), and 3 (very high cost). The model output value will be one of these four classes.
The Regression model is wrong, the Regression model uses historical data for model training. But it predicts the output numeric value, not the class or classes.
The Clustering model is wrong, the Clustering model clusters unlabeled data into groups based on some common properties.
An unsupervised model uses unlabeled data.
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