Instructions: In the next exercise, you will use a known dataset used as an example for classification problems. Refer to: Index of /ml/machine-learning-databases/balance-scale (uci.edu). The problem has 3 classes and 4 input features. The possible classes are (B: balanced, L: left, R: right), and the 4 input features are: (LW: left weight, RW: right weight, LD: left distance, RD: Right distance). The balance shows up the value ‘B’ when LW + LD = RD + RW. Each raw of the file data is constructed as the following: Class, LW, LD, RW, RD. Example: B, 3, 2, 4, 1. In addition, all the input features might have values 1, ..., 5. For more information refer to website given before. Remark: you are free to propose a preprocessing of the input features, also you’re advised to try different values of the learning rate and observe the training curve. Remark: in your code, you are asked to use python, numpy and matplotlib. You shouldn’t use libraries like scikit, tensorflow, pytorch, or any other library dedicated to AI. Remark: Provide an intelligible and commented algorithm that shows your deep understanding of your code. Remark: as far as the normalized algorithm is concerned, it is usual to prevent a possible division by zero, so a small real positive number is added to the divisor.
Concepts in Designing Database
A database design is the process of data organization based on a database model. The process deals with identifying what data should be stored in a database and how data elements relate to each other.
Entity Relationship Diagram
Complex real-world applications call for large volumes of data. Therefore, it is necessary to build a great database to store data safely and coherently. The ER data model aids in the process of database design. It helps outline the structure of an organization’s database by understanding the real-world interactions of objects related to the data. For example, if a school is tasked to store student information, then analyzing the correlation between the students, subjects, and teachers would help identify how the data needs to be stored.
Instructions: In the next exercise, you will use a known dataset used as an example for classification problems.
Refer to: Index of /ml/machine-learning-
The problem has 3 classes and 4 input features. The possible classes are (B: balanced, L: left, R: right), and the 4 input features are: (LW: left weight, RW: right weight, LD: left distance, RD: Right distance). The balance shows up the value ‘B’ when LW + LD = RD + RW.
Each raw of the file data is constructed as the following: Class, LW, LD, RW, RD.
Example: B, 3, 2, 4, 1. In addition, all the input features might have values 1, ..., 5. For more information refer to website given before.
Remark: you are free to propose a preprocessing of the input features, also you’re advised to try different values of the learning rate and observe the training curve.
Remark: in your code, you are asked to use python, numpy and matplotlib. You shouldn’t use libraries like scikit, tensorflow, pytorch, or any other library dedicated to
Remark: Provide an intelligible and commented
Remark: as far as the normalized algorithm is concerned, it is usual to prevent a possible division by zero, so a small real positive number is added to the divisor.
Exercise 1: Let’s first study the simple problem in which only 2 classes (R: Right, E: else) are observed. The balance shows up either C1 (R), when RW + RD > 3 or C2 (E) otherwise. The problem now has only two input features RW and RD.
1- Write a function to create the new data set.
2- Write a function to plot the patterns relatives to classes R and E in different colors and different shapes. Is the problem linearly separable? Give an explanation.
Remark: you can include the figure and your response into a markdown cell or provide them in a separate pdf file
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