Machine Learning Classification Algorithms – Part III

See Part I and Part II to get insight on Supervised Learning.

Types of Classification

Based on the number and level of classes present in the dataset, there are three types of classification.

Binary Classification

This type of classification has only two categories. Usually, they are boolean values – 1 or 0, True or False, High or Low. Some examples where such a classification could be used is in cancer detection or email spam detection where the labels would be positive or negative for cancer and spam or not spam for spam detection.

Let us take an example. We are using a breast cancer detection dataset that can be downloaded from here.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
data= pd.read_csv(“data.csv”)
data.head()

read_data_for_classification.py hosted with ❤ by GitHub

	id	diagnosis	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	symmetry_mean	fractal_dimension_mean	radius_se	texture_se	perimeter_se	area_se	smoothness_se	compactness_se	concavity_se	concave points_se	symmetry_se	fractal_dimension_se	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst
0	842302	M	17.99	10.38	122.80	1001.0	0.11840	0.27760	0.3001	0.14710	0.2419	0.07871	1.0950	0.9053	8.589	153.40	0.006399	0.04904	0.05373	0.01587	0.03003	0.006193	25.38	17.33	184.60	2019.0	0.1622	0.6656	0.7119	0.2654	0.4601	0.11890
1	842517	M	20.57	17.77	132.90	1326.0	0.08474	0.07864	0.0869	0.07017	0.1812	0.05667	0.5435	0.7339	3.398	74.08	0.005225	0.01308	0.01860	0.01340	0.01389	0.003532	24.99	23.41	158.80	1956.0	0.1238	0.1866	0.2416	0.1860	0.2750	0.08902
2	84300903	M	19.69	21.25	130.00	1203.0	0.10960	0.15990	0.1974	0.12790	0.2069	0.05999	0.7456	0.7869	4.585	94.03	0.006150	0.04006	0.03832	0.02058	0.02250	0.004571	23.57	25.53	152.50	1709.0	0.1444	0.4245	0.4504	0.2430	0.3613	0.08758
3	84348301	M	11.42	20.38	77.58	386.1	0.14250	0.28390	0.2414	0.10520	0.2597	0.09744	0.4956	1.1560	3.445	27.23	0.009110	0.07458	0.05661	0.01867	0.05963	0.009208	14.91	26.50	98.87	567.7	0.2098	0.8663	0.6869	0.2575	0.6638	0.17300
4	84358402	M	20.29	14.34	135.10	1297.0	0.10030	0.13280	0.1980	0.10430	0.1809	0.05883	0.7572	0.7813	5.438	94.44	0.011490	0.02461	0.05688	0.01885	0.01756	0.005115	22.54	16.67	152.20	1575.0	0.1374	0.2050	0.4000	0.1625	0.2364	0.07678

sns.scatterplot(x=”radius_mean”,y=”texture_mean”,hue=”diagnosis”,data=data)

Fig. 2. Scatter Plot – Texture Mean vs. Radius Mean

Here you can see the two ‘classes’ – ‘M’ stands for malignant and ‘B’ stands for benign. As you can see, the classes are well divided and are easily differentiable to the naked eye for these two features. However, this will not be true for all pairs of features.

Models that can be used for such a classification are:

• Logistic Regression
• Support Vector Classifiers

You can also use Decision Trees, Random Forests and other algorithms but Logistic Regression and Support Vector Classification are used exclusively for binary classification.

Stay tuned for the next installment in this series to learn about Multi-class Classification.

Visit QuantInsti for additional insight on this topic: https://blog.quantinsti.com/machine-learning-classification/

Disclaimer: All investments and trading in the stock market involve risk. Any decisions to place trades in the financial markets, including trading in stocks or other financial instruments is a personal decision that should only be made after thorough research, including a personal risk and financial assessment and the engagement of professional assistance to the extent you believe necessary. The trading strategies or related information mentioned in this article is for informational purposes only.