Machine Learning

Question: Which of them is a supervised classification problem?

Answer: Using labeled financial data to predict whether the value of a stock will go up or go down next week.

"Exactly! In this example, there are two discrete, qualitative outcomes: the stock market going up, and the stock market going down. This can be represented using a binary variable, and is an application perfectly suited for classification."

So this supervised learning can be used to predict whether something will go up or down (stock prices, temperature, number of sales, etc. To do this you need labelled data because, this is a supervised learning. This can be used whether spin is up or down kind of problem.

Where this type of model can be used in physics, chemistry, mathematics, etc? Is there such studies?

A common data sets used is Iris dataset.

This data set contain

1. petal length 
2. petal width
3. Setal length
4. Setal width

Here, in this blog, you can see how these four quantities are measured. 

Target variable?

In machine learning, a target variable is one that should be the output (after the analysis)

Three different flower species. 

          0. Setosa
          1. Versicolor
          2. Virginica
Here, we are going to give the four data of an unknown flower (to the trained model), and we are going to find out which flower it is (the model will give 0 or 1 or 2 based on the input).

iris.data.shape gives (150, 4) which means that there are 154 rows (data) with four different information (here petal length, petal width, Setal length, Setal width).  

iris.data.shape   

(150,4)

iris.target_names

(names of the target in an array)

(to be continued)