Build K-NN and Decision Tree Classifiers for SAT Problem
Buget €30-250 EUR
Deadline for this project is 13/04/2022.
This project will be coded using Python.
Read the notes section and make sure it is understood.
Using Scikit-learn (and pandas to import the dataset), train and evaluate K-NN and decision tree classifiers using 70% of the
dataset from training and 30% for testing. For this part of the project, we are not interested
in optimising the parameters; we just want to get an idea of the dataset.
Compare the results of both classifiers in a # comment.
Following this, carry out the following methods, and explain the results of each one using a # comment at the start of each code segment:
● Hold-out and cross-validation.
● Hyper-parameter tuning.
● Feature reduction.
● Feature normalisation.
Scikit-learn (and pandas just to import the dataset) MUST BE USED, but you can use other libraries along with it.
The dataset you will use is included in the files attached.
The Boolean satisfiability (SAT) problem consists in determining whether a Boolean
formula F is satisfiable or not. F is represented by a pair (X, C), where X is a set of Boolean
variables and C is a set of clauses in Conjunctive Normal Form (CNF). Each clause is a
disjunction of literals (a variable or its negation). This problem is well known as the classic NP-complete
Recent advances in supervised learning have provided powerful techniques for classifying
problems. In this project, we see the SAT problem as a classification problem. Given a
Boolean formula (represented by a vector of features), we are asked to predict if it is
satisfiable or not.
In this project, we represent SAT problems with a vector of 327 features with general
information about the problem, e.g., number of variables, number of clauses, fraction of
horn clauses in the problem, etc.
The CSV dataset file attached contains 1929 rows and 328 columns. The first 327 columns contain the features. The last column (‘target’) contains the label; 0 for unsatisfiable and 1 for satisfiable.
18 freelanceri licitează în medie 144€ pentru acest proiect
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