Day 53: 60 days of Data Science and Machine Learning Series
Yellowbrick combines scikit-learn with matplotlib and provides the scikit-learn API to produce visualizations for the machine learning workflow. A good reference point to understand the vastness of Yellowbrick and how to use it —
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You can install yellowbrick using the command below —
$ pip install yellowbrickIn this post, we will build, evaluate a logistic regression classifier and perform visualizations using Yellowbricks data visualization tools/modules.
Let’s dive in!
Import necessary libraries
import matplotlib.pyplot as plt
%matplotlib inline
import pandas as pd
import warnings
warnings.simplefilter('ignore')
import numpy as np
import yellowbrick as yb
from sklearn.model_selection import train_test_split
from yellowbrick.classifier import ROCAUC
from sklearn.linear_model import LogisticRegression
from yellowbrick.classifier import ClassificationReport
from yellowbrick.classifier import ConfusionMatrix
from sklearn.model_selection import StratifiedKFold
from yellowbrick.model_selection import CVScores
from yellowbrick.classifier import ClassBalance
from yellowbrick.classifier import DiscriminationThresholdLoad the data
# Load the classification data set
data = pd.read_csv('Path to the file /data.csv')ROC/AUC Plots
X_train, X_test,y_train,y_test = train_test_split(X,y,test_size=0.2)
v = ROCAUC(LogisticRegression(),size=(800,600))v.fit(X_train,y_train)
v.score(X_test,y_test)
g= v.poof()Output —
Classification Report and Confusion Matrix
v = ClassificationReport(LogisticRegression(),classes=classes,support=True)
v.fit(X_train,y_train)
v.score(X_test,y_test)
g=v.poof()Output —
c = ConfusionMatrix(v,classes=[0,1])
c.score(X_test,y_test)
c.poof()Output —
Cross-validation Scores
_,ax = plt.subplots()
cv = StratifiedKFold(12)
v = CVScores(LogisticRegression(),ax=ax,cv=cv,scoring='f1_weighted',size=(800,600))
v.fit(X,y)
v.poof()Output —
Evaluating Class Balance
v = ClassBalance(labels=classes)
v.fit(y_train,y_test)
v.poof()Output —
Discrimination Threshold
v = DiscriminationThreshold(LogisticRegression(),size=(800,600))
v.fit(X_train,y_train)
v.poof()Output —
Learnings —
How to build, evaluate a logistic regression classifier and perform visualizations using yellowbricks data visualization tools/modules.
Day 54: Coming soon!
Follow and Stay tuned. Keep coding :)
For other projects, tune to —
Build Machine Learning Pipelines( With Code)
Recurrent Neural Network with Keras
Clustering Geolocation Data in Python using DBSCAN and K-Means
Facial Expression Recognition using Keras
Hyperparameter Tuning with Keras Tuner
Custom Layers in Keras
That’s it fellas. Peace out and keep coding :)
Stay Tuned and of-course let me end this post with a quote by Steve Jobs ;)
“You have to be burning with an idea, or a problem, or a wrong that you want to right. If you’re not passionate enough from the start, you’ll never stick it out.”
