Introduction to Machine Learning#

One of the last stages of the data science life cycle is the Data Modeling. Machine learning algorithms are a set of statistical techniques that build mathematical models from training data. These algorithms come in two types:

Supervised : these algorithms are used when we want to predict a response column.
Unsupervised : these algorithms are used when we want to detect anomalies or when we want to segment the data. No response column is needed.

Supervised Learning#

Supervised Learning techniques map an input to an output based on some example dataset. This type of learning consists of two main types:

Regression : The Response is numerical (Linear Regression, SVM Regression, RF Regression…)
Classification : The Response is categorical (Gradient Boosting, Naive Bayes, Logistic Regression…)

For example, predicting the total charges of a Telco customer using their tenure would be a type of regression. The following code is drawing a linear regression using the ‘TotalCharges’ as a function of the ‘tenure’ in the Telco Churn Dataset.

[1]:

import verticapy as vp
vp.set_option("plotting_lib","highcharts")
churn = vp.read_csv("data/churn.csv")

from verticapy.learn.linear_model import LinearRegression
vp.drop("LR_churn")
model = LinearRegression("LR_churn")
model.fit(churn, ["tenure"], "TotalCharges")
model.plot()

[1]:

In contrast, when we have to predict a categorical column, we’re dealing with classification.

In the following example, we use a Linear Support Vector Classification (SVC) to predict the species of a flower based on its petal and sepal lengths.

[2]:

from verticapy.datasets import load_iris
iris = load_iris()
iris.one_hot_encode()

/opt/venv/lib/python3.10/site-packages/verticapy/core/vdataframe/_encoding.py:123: Warning: The vDataColumn '"Id"' was ignored because of its high cardinality.
Increase the parameter 'max_cardinality' to solve this issue or use directly the vDataColumn get_dummies method.
warnings.warn(warning_message, Warning)
/opt/venv/lib/python3.10/site-packages/verticapy/core/vdataframe/_encoding.py:123: Warning: The vDataColumn '"PetalLengthCm"' was ignored because of its high cardinality.
Increase the parameter 'max_cardinality' to solve this issue or use directly the vDataColumn get_dummies method.
warnings.warn(warning_message, Warning)
/opt/venv/lib/python3.10/site-packages/verticapy/core/vdataframe/_encoding.py:123: Warning: The vDataColumn '"PetalWidthCm"' was ignored because of its high cardinality.
Increase the parameter 'max_cardinality' to solve this issue or use directly the vDataColumn get_dummies method.
warnings.warn(warning_message, Warning)
/opt/venv/lib/python3.10/site-packages/verticapy/core/vdataframe/_encoding.py:123: Warning: The vDataColumn '"SepalLengthCm"' was ignored because of its high cardinality.
Increase the parameter 'max_cardinality' to solve this issue or use directly the vDataColumn get_dummies method.
warnings.warn(warning_message, Warning)
/opt/venv/lib/python3.10/site-packages/verticapy/core/vdataframe/_encoding.py:123: Warning: The vDataColumn '"SepalWidthCm"' was ignored because of its high cardinality.
Increase the parameter 'max_cardinality' to solve this issue or use directly the vDataColumn get_dummies method.
warnings.warn(warning_message, Warning)

[2]:

	123 Id Integer	123 PetalLengthCm Numeric(8)	123 PetalWidthCm Numeric(8)	123 SepalLengthCm Numeric(8)	123 SepalWidthCm Numeric(8)	Abc Species Varchar(30)	123 Species_Iris-setosa Integer	123 Species_Iris-versicolor Integer
1	1	1.4	0.2	5.1	3.5	Iris-setosa	1	0
2	2	1.4	0.2	4.9	3.0	Iris-setosa	1	0
3	3	1.3	0.2	4.7	3.2	Iris-setosa	1	0
4	4	1.5	0.2	4.6	3.1	Iris-setosa	1	0
5	5	1.4	0.2	5.0	3.6	Iris-setosa	1	0
6	6	1.7	0.4	5.4	3.9	Iris-setosa	1	0
7	7	1.4	0.3	4.6	3.4	Iris-setosa	1	0
8	8	1.5	0.2	5.0	3.4	Iris-setosa	1	0
9	9	1.4	0.2	4.4	2.9	Iris-setosa	1	0
10	10	1.5	0.1	4.9	3.1	Iris-setosa	1	0
11	11	1.5	0.2	5.4	3.7	Iris-setosa	1	0
12	12	1.6	0.2	4.8	3.4	Iris-setosa	1	0
13	13	1.4	0.1	4.8	3.0	Iris-setosa	1	0
14	14	1.1	0.1	4.3	3.0	Iris-setosa	1	0
15	15	1.2	0.2	5.8	4.0	Iris-setosa	1	0
16	16	1.5	0.4	5.7	4.4	Iris-setosa	1	0
17	17	1.3	0.4	5.4	3.9	Iris-setosa	1	0
18	18	1.4	0.3	5.1	3.5	Iris-setosa	1	0
19	19	1.7	0.3	5.7	3.8	Iris-setosa	1	0
20	20	1.5	0.3	5.1	3.8	Iris-setosa	1	0
21	21	1.7	0.2	5.4	3.4	Iris-setosa	1	0
22	22	1.5	0.4	5.1	3.7	Iris-setosa	1	0
23	23	1.0	0.2	4.6	3.6	Iris-setosa	1	0
24	24	1.7	0.5	5.1	3.3	Iris-setosa	1	0
25	25	1.9	0.2	4.8	3.4	Iris-setosa	1	0
26	26	1.6	0.2	5.0	3.0	Iris-setosa	1	0
27	27	1.6	0.4	5.0	3.4	Iris-setosa	1	0
28	28	1.5	0.2	5.2	3.5	Iris-setosa	1	0
29	29	1.4	0.2	5.2	3.4	Iris-setosa	1	0
30	30	1.6	0.2	4.7	3.2	Iris-setosa	1	0
31	31	1.6	0.2	4.8	3.1	Iris-setosa	1	0
32	32	1.5	0.4	5.4	3.4	Iris-setosa	1	0
33	33	1.5	0.1	5.2	4.1	Iris-setosa	1	0
34	34	1.4	0.2	5.5	4.2	Iris-setosa	1	0
35	35	1.5	0.1	4.9	3.1	Iris-setosa	1	0
36	36	1.2	0.2	5.0	3.2	Iris-setosa	1	0
37	37	1.3	0.2	5.5	3.5	Iris-setosa	1	0
38	38	1.5	0.1	4.9	3.1	Iris-setosa	1	0
39	39	1.3	0.2	4.4	3.0	Iris-setosa	1	0
40	40	1.5	0.2	5.1	3.4	Iris-setosa	1	0
41	41	1.3	0.3	5.0	3.5	Iris-setosa	1	0
42	42	1.3	0.3	4.5	2.3	Iris-setosa	1	0
43	43	1.3	0.2	4.4	3.2	Iris-setosa	1	0
44	44	1.6	0.6	5.0	3.5	Iris-setosa	1	0
45	45	1.9	0.4	5.1	3.8	Iris-setosa	1	0
46	46	1.4	0.3	4.8	3.0	Iris-setosa	1	0
47	47	1.6	0.2	5.1	3.8	Iris-setosa	1	0
48	48	1.4	0.2	4.6	3.2	Iris-setosa	1	0
49	49	1.5	0.2	5.3	3.7	Iris-setosa	1	0
50	50	1.4	0.2	5.0	3.3	Iris-setosa	1	0
51	51	4.7	1.4	7.0	3.2	Iris-versicolor	0	1
52	52	4.5	1.5	6.4	3.2	Iris-versicolor	0	1
53	53	4.9	1.5	6.9	3.1	Iris-versicolor	0	1
54	54	4.0	1.3	5.5	2.3	Iris-versicolor	0	1
55	55	4.6	1.5	6.5	2.8	Iris-versicolor	0	1
56	56	4.5	1.3	5.7	2.8	Iris-versicolor	0	1
57	57	4.7	1.6	6.3	3.3	Iris-versicolor	0	1
58	58	3.3	1.0	4.9	2.4	Iris-versicolor	0	1
59	59	4.6	1.3	6.6	2.9	Iris-versicolor	0	1
60	60	3.9	1.4	5.2	2.7	Iris-versicolor	0	1
61	61	3.5	1.0	5.0	2.0	Iris-versicolor	0	1
62	62	4.2	1.5	5.9	3.0	Iris-versicolor	0	1
63	63	4.0	1.0	6.0	2.2	Iris-versicolor	0	1
64	64	4.7	1.4	6.1	2.9	Iris-versicolor	0	1
65	65	3.6	1.3	5.6	2.9	Iris-versicolor	0	1
66	66	4.4	1.4	6.7	3.1	Iris-versicolor	0	1
67	67	4.5	1.5	5.6	3.0	Iris-versicolor	0	1
68	68	4.1	1.0	5.8	2.7	Iris-versicolor	0	1
69	69	4.5	1.5	6.2	2.2	Iris-versicolor	0	1
70	70	3.9	1.1	5.6	2.5	Iris-versicolor	0	1
71	71	4.8	1.8	5.9	3.2	Iris-versicolor	0	1
72	72	4.0	1.3	6.1	2.8	Iris-versicolor	0	1
73	73	4.9	1.5	6.3	2.5	Iris-versicolor	0	1
74	74	4.7	1.2	6.1	2.8	Iris-versicolor	0	1
75	75	4.3	1.3	6.4	2.9	Iris-versicolor	0	1
76	76	4.4	1.4	6.6	3.0	Iris-versicolor	0	1
77	77	4.8	1.4	6.8	2.8	Iris-versicolor	0	1
78	78	5.0	1.7	6.7	3.0	Iris-versicolor	0	1
79	79	4.5	1.5	6.0	2.9	Iris-versicolor	0	1
80	80	3.5	1.0	5.7	2.6	Iris-versicolor	0	1
81	81	3.8	1.1	5.5	2.4	Iris-versicolor	0	1
82	82	3.7	1.0	5.5	2.4	Iris-versicolor	0	1
83	83	3.9	1.2	5.8	2.7	Iris-versicolor	0	1
84	84	5.1	1.6	6.0	2.7	Iris-versicolor	0	1
85	85	4.5	1.5	5.4	3.0	Iris-versicolor	0	1
86	86	4.5	1.6	6.0	3.4	Iris-versicolor	0	1
87	87	4.7	1.5	6.7	3.1	Iris-versicolor	0	1
88	88	4.4	1.3	6.3	2.3	Iris-versicolor	0	1
89	89	4.1	1.3	5.6	3.0	Iris-versicolor	0	1
90	90	4.0	1.3	5.5	2.5	Iris-versicolor	0	1
91	91	4.4	1.2	5.5	2.6	Iris-versicolor	0	1
92	92	4.6	1.4	6.1	3.0	Iris-versicolor	0	1
93	93	4.0	1.2	5.8	2.6	Iris-versicolor	0	1
94	94	3.3	1.0	5.0	2.3	Iris-versicolor	0	1
95	95	4.2	1.3	5.6	2.7	Iris-versicolor	0	1
96	96	4.2	1.2	5.7	3.0	Iris-versicolor	0	1
97	97	4.2	1.3	5.7	2.9	Iris-versicolor	0	1
98	98	4.3	1.3	6.2	2.9	Iris-versicolor	0	1
99	99	3.0	1.1	5.1	2.5	Iris-versicolor	0	1
100	100	4.1	1.3	5.7	2.8	Iris-versicolor	0	1

Rows: 1-100 of 150 | Columns: 8

[3]:

from verticapy.learn.svm import LinearSVC
vp.drop("svc_setosa_iris")
model = LinearSVC("svc_setosa_iris")
model.drop()
model.fit(iris, ["PetalLengthCm", "SepalLengthCm"], "Species_Iris-setosa")
model.plot()

[3]:

When we have more than two categories, we use the expression ‘Multiclass Classification’ instead of ‘Classification’.

Unsupervised Learning#

These algorithms are to used to segment the data (k-means, DBSCAN, etc.) or to detect anomalies (Local Outlier Factor, Z-Score Techniques…). In particular, they’re useful for finding patterns in data without labels. For example, let’s use a k-means algorithm to create different clusters on the Iris dataset. Each cluster will represent a flower’s species.

[4]:

from verticapy.learn.cluster import KMeans
vp.drop("KMeans_iris")
model = KMeans("KMeans_iris", n_cluster = 3)
model.fit(iris, ["PetalLengthCm", "SepalLengthCm"])
model.plot()

[4]:

In this section, we went over a few of the many ML algorithms available in VerticaPy. In the next lesson, we’ll cover creating a regression model.