What Machine Learning Models have in common

Introduction

It's tough to find things that always work the same way in programming.

The steps of a Machine Learning (ML) model can be an exception.

Each time we want to compute a model (mathematical equation) and make predictions with it, we would always make the following steps:

model.fit() → to compute the numbers of the mathematical equation..
model.predict() → to calculate predictions through the mathematical equation.
model.score() → to measure how good the model's predictions are.

And I am going to show you this with 3 different ML models.

DecisionTreeClassifier()
RandomForestClassifier()
LogisticRegression()

Load the Data

But first, let's load a dataset from CIS executing the lines of code below:

The goal of this dataset is

To predict internet_usage of people (rows)

Based on their socio-demographical characteristics (columns)

import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/jsulopz/data/main/internet_usage_spain.csv')
df.head()

	internet_usage	sex	age	education
0	0	Female	66	Elementary
1	1	Male	72	Elementary
2	1	Male	48	University
3	0	Male	59	PhD
4	1	Female	44	PhD

Data Preprocessing

We need to transform the categorical variables to dummy variables before computing the models:

df = pd.get_dummies(df, drop_first=True)
df.head()

Feature Selection

Now we separate the variables on their respective role within the model:

target = df.internet_usage
explanatory = df.drop(columns='internet_usage')

ML Models

Decision Tree Classifier

from sklearn.tree import DecisionTreeClassifier

model = DecisionTreeClassifier()
model.fit(X=explanatory, y=target)

pred_dt = model.predict(X=explanatory)
accuracy_dt = model.score(X=explanatory, y=target)

Support Vector Machines

from sklearn.svm import SVC

model = SVC()
model.fit(X=explanatory, y=target)

pred_sv = model.predict(X=explanatory)
accuracy_sv = model.score(X=explanatory, y=target)

K Nearest Neighbour

from sklearn.neighbors import KNeighborsClassifier

model = KNeighborsClassifier()
model.fit(X=explanatory, y=target)

pred_kn = model.predict(X=explanatory)
accuracy_kn = model.score(X=explanatory, y=target)

The only thing that changes are the results of the prediction. The models are different. But they all follow the same steps that we described at the beginning:

model.fit() → to compute the mathematical formula of the model
model.predict() → to calculate predictions through the mathematical formula
model.score() → to get the success ratio of the model

Comparing Predictions

You may observe in the following table how the different models make different predictions, which often doesn't coincide with reality (misclassification).

For example, model_svm doesn't correctly predict the row 214; as if this person used internet pred_svm=1, but they didn't: internet_usage for 214 in reality is 0.

df_pred = pd.DataFrame({'internet_usage': df.internet_usage,
                        'pred_dt': pred_dt,
                        'pred_svm': pred_sv,
                        'pred_lr': pred_kn})

df_pred.sample(10, random_state=7)

	internet_usage	pred_dt	pred_svm	pred_lr
214	0	0	1	0
2142	1	1	1	1
1680	1	0	0	0
1522	1	1	1	1
325	1	1	1	1
2283	1	1	1	1
1263	0	0	0	0
993	0	0	0	0
26	1	1	1	1
2190	0	0	0	0

Choose Best Model

Then, we could choose the model with a higher number of successes on predicting the reality.

df_accuracy = pd.DataFrame({'accuracy': [accuracy_dt, accuracy_sv, accuracy_kn]},
                           index = ['DecisionTreeClassifier()', 'SVC()', 'KNeighborsClassifier()'])

df_accuracy

	accuracy
DecisionTreeClassifier()	0.859878
SVC()	0.783707
KNeighborsClassifier()	0.827291

Which is the best model here?

Let me know in the comments below ↓

Why do all Machine Learning models follow the same steps?

Comments (1)

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Introduction

Load the Data

Data Preprocessing

Feature Selection

ML Models

Decision Tree Classifier

Support Vector Machines

K Nearest Neighbour

Comparing Predictions

Choose Best Model

Command Palette

Comments (1)

Machine Learning

#05 | The k-Means & Unsupervised Clustering Models

More from this blog

Introduction

Load the Data

Data Preprocessing

Feature Selection

ML Models

Decision Tree Classifier

Support Vector Machines

K Nearest Neighbour

Comparing Predictions

Choose Best Model