I’ve created these step-by-step machine learning algorith implementations in Python for everyone who is new to the field and might be confused with the different steps.
Logistic Regression is a very old model (think ~200ish years) that still works pretty well for many different problems.
Check out this page to learn about the history Logistic of Regression .
Its mathematical foundations, high accuracy and high scalability makes it favorable in many cases.
And on top of that, it produces statistical outputs and probability calculations and sometimes that’s just what you need (compared to just labels).
With the help of regularization, this model can be a strong member of the Machine Learning toolset when you have Data Science problems with linear decision boundary relations.
Check out the pros & cons of Logistic Regression to discover more.
10 mins
Advanced
Provided by HolyPython.com
I’ve split up Logistic Regression implementation to 2 different categories here:
(Red for the actual machine learning work and black font signifies preparation phase)
Down the page I’ve also color coded the steps in a different way to group similar steps with each other.
pandas can be useful for constructing dataframes and scikit learn is the ultimate library for simple machine learning operations, learning and practicing machine learning.
Reading data is simple but there can be important points such as: dealing with columns, headers, titles, constructing data frames etc.
Machine Learning models can be created with a very simple and straight-forward process using scikitlearn. We will create a Logistic Regression object from the LogisticRegression of scikitlearn.linear_model library.
Once the model is ready, predictions can be done on the test part of the data. Furthermore, I enjoy predicting foreign values that are not in the initial dataset just to observe the outcomes the model creates. .predict method is used for predictions.
We need a nice dataset that’s sensible to analyze with machine learning techniques, particularly logistic regression in this case. Scikitlearn has some cool sample data as well.
Even splitting data is made easy with Scikit-learn, for this operation we will use train_test_module from scikitlearn library.
Machine Learning models are generally fit by training data. This is the part where training of the model takes place and we will do the same for our Logistic Regression model.
Finally, scikitlearn library’s metrics module is very useful to test the accuracy of the model’s predictions. This part could be done manually as well but metrics module brings lots of functionality and simplicity to the table.
First the import part for libraries:
###Importing Libraries
import pandas as pd
from sklearn import datasets
from sklearn.linear_model import LogisticRegression as logreg
from sklearn.model_selection import train_test_split as tts
from sklearn import metrics
It’s time to find some data to work with. For the simplicity I will suggest using pre-included datasets library in scikitlearn. They are great for practice and everything is already taken care. So, there won’t be a complication such as missing values or invalid characters etc. while you’re learning.
Let’s import the iris dataset:
###Importing Dataset
iris = datasets.load_iris()
Now we can get the data ready:
Pandas DataFrame class is used to construct a data frame. Data frames are very useful when working with large datasets with different titles.
In case of machine learning algorithms: you usually have feature(s) and an outcome or multiple outcomes to work with, this mean different titles and sometimes different types of data. That’s why DataFrame becomes the perfect structure to work with.
###Constructing Data Frame
data = pd.DataFrame({"sl":iris.data[:,0], "sw":iris.data[:,1], "pl":iris.data[:,2], "pw":iris.data[:,3], 'species': iris.target})
# print(data["species"])
This is another standard Machine Learning step.
We need to split data so that there are:
It’s a rather simple process (step) thanks to Scikit learn’s train_test_split module.
###Splitting train/test data
x=data[['sl','sw','pl','pw']]
y=data["species"]
X_tr, X_ts, y_tr, y_ts = tts(X,y, test_size=30/100, random_state=None)
Now we can create a Logistic Regression object and put machine learning to work using the training data:
###Creating Logistic Regression Model
LOGR = logreg()
###Training the Model
LOGR.fit(X_tr,y_tr)
###Making Predictions
y_pr=DT.predict(X_ts)
print(y_pr)
###Evaluating Prediction Accuracy
print("Acc %:",metrics.accuracy_score(y_ts, y_pr)*100)
###Making Prediction with Foreign Data
print(DT.predict([[1,1,0.5,6]]))
You can see the full one piece code in this page: Logistic Regression Simple Implementation