Classification vs Regression Detailed Comparison

ML Types

1. Course
Outcomes
Aftercompletionofthiscourse,studentswillbeableto
Understandmachine-learningconcepts.
UnderstandandimplementClassificationconcepts.
UnderstandandanalysethedifferentRegression
algorithms.
ApplytheconceptofUnsupervisedLearning.
ApplytheconceptsofArtificialNeuralNetworks.

2. Topics -
Supervised
Learning
Classification Techniques:
Naive Bayes Classification
Fitting Multivariate Bernoulli
Distribution
Gaussian Distribution and
Multinomial Distribution
K-Nearest Neighbours
Decision tree
Random Forest
EnsembleLearning
Support Vector Machines
Evaluation metrics for
Classification Techniques:
Confusion Matrix, Accuracy,
Precision, Recall, F1 Score,
Threshold, AUC-ROC
Regression Techniques:
Basic concepts and
applications of Regression
Simple Linear Regression -
Gradient Descent and Normal
Equation Method
Multiple Linear Regression
Non-Linear Regression
LinearRegression with
Regularization
Overfitting and Underfitting
Hyperparametertuning
Evaluation Measures for
Regression Techniques: MSE,
RMSE, MAE, R2

3. Algorithms
Machine
Learning
Techniques
Supervised Learning
(Labeled Data)
Regression Techniques
(Continuous Data)
Classification
Techniques (Categorical
Data)
Unsupervised
Learning (Unlabeled
Data)
Clustering
Association Rule Mining

4. Supervised
Learning

5. Algorithms
Regression
Techniques
(Continuous
Data)
Simple Linear Regression
Normal Equation Method
Gradient Descent
Method
Regularization &
HyperparameterTuning
Lasso Regularization –L1
Regularization
Ridge Regularization –L2
Regularization
Elastic Net Regularization
–L1 and L2 Regularization
Multiple Linear
Regression
Non Linear Regression /
Polynomial Regression
Quadratic Regression
Cubic Regression
nth degree Regression

6. Algorithms
Classification
Techniques
(Categorical
Data)
Linear Models
Logistic Regression
Binomial Logistic
Regression
Multinomial Logistic
Regression
Ordinal Logistic
Regression
Support Vector
Machine (SVM)
Non-linear
Models
K-Nearest
Neighbours (KNN)
Decision Tree
Naïve Bayes
Multivariate Bernoulli
Distribution
Gaussian Distribution
Multinomial
Distribution
Ensemble Method
Random Forest
Gradient Boosting

7. Performance Evaluation Measures
for Supervised Learning Algorithm
Regression
Techniques
(Continuous Data)
Mean Absolute
Error (MAE)
Mean Squared
Error (MSE)
Root Mean
Squared Error
(RMSE)
R-squared
(Coefficient of
Determination)
(R2)
Classification
Techniques
(Categorical Data)
Confusion
Matrix
True Positive (TP)
True Negative (TN)
False Positive (FP)
False Negative (FN)
Accuracy Precision
Recall /
Sensitivity/ True
Positive Rate
F1
Score
Threshold
False
Positive
Rate (FPR)
AUC-
ROC

8. Simple Linear
Regression
Multiple Regression PolynomialRegression
X / Input/
Independent
Variable
1 >=2or >1 (UptoN ) 1
Y / Output/ target
variable/
Dependent
Variable
1 1 1
Line Equation Y= mX+ c Y = a
0+ a
1X
1+ a
2X
2… + a
nX
n
Y = a
0+ a
1X + a
2X
2
… + a
nx
n
Type ofLine
Equation
Linear Line Equation Linear Line Equation
Polynomial Line Equation
with Degree n
No of Coefficient 1 =No ofInput (i.eX) = No of Degree(n)
No of intersection
point
1 1 1
Database
X Y X YX1X2Y