MLClassifiersInCPP

3 classifiers for the LingSpam dataset, using tf-idf features, written in C++.

1. k-NN classifier
2. Naive Bayes classifier
3. Baseline classifier

The k-NN classifier either uses Euclidean distances or cosine similarity as the metric measure.

The Baseline classifier is a dummy classifier that either classifies all the data with the most frequent label in the training set or with random labels altogether.

The program is tested on a Linux machine.

How to compile

Run the script "compile.sh". Type:

./compile.sh

How to run

1. First construct the dataset. Run:

./bin/construct_dataset.o

2. Then, classify the dataset, with the 3 classifiers. Run:

./bin/Main.o

Experiment results

Classifier	Accuracy	Precision	Recall	test	wrong	TP	TN	FP	FN
10-NN Classifier using Euclidean distances metric	91.35 %	65.75 %	100 %	289	25	48	25	216	0
1-NN Classifier using Euclidean distances metric	88.93 %	60 %	100 %	289	32	48	32	209	0
10-NN Classifier using Cosine similarity metric	93.08 %	71.88 %	95.83 %	289	20	46	18	223	2
1-NN Classifier using Cosine similarity metric	78.2 %	37.29 %	45.83 %	289	63	22	37	204	26
Naive Bayes Classifier	96.19 %	93.02 %	83.33 %	289	11	40	3	238	8
Baseline Classifier (Most Frequent label strategy)	83.4 %	65.75 %	0 %	289	48	0	0	241	48
Baseline Classifier (Random labels strategy)	46.71 %	13.7 %	41.67 %	289	154	20	126	115	28