Overview of Machine Learning Algorithms

\(\color{red}{\text{ Draft: Work in Progress..... }}\)

Learning Styles:

  • Supervised Learning
    • The values of target variable, in training set are known.
  • Unsupervised Learning
    • data is not labeled and outcomes are unknown.
    • Seek to find hidden structure in unlabeled data
    • Example: Clustering algorithms
  • Semi-supervised Learning
    • Uses both labeled and unlabeled data to improve supervised learning.
    • Example: in Deep learning Generative models


  • Classification problem: The target variable is qualitative (categorical/nominal)
    • Binary- Two possible outcomes only
      • e.g. Yes/No, 0/1
    • Multi-Class- More than 2 outcomes
      • e.g. Positive, Neutral or Negative customer sentiment/feedback
  • Regression problem: The target is quantitative (discrete or continuous)
    • Can take any real number in real line
      • e.g. Stock prices, estimated revenue, interest rates, price of house, Number of accidents
  • Others:
    • Clustering
    • Dimension reduction

import pandas as pd
#from sklearn.cross_validation import train_test_split, StratifiedKFold 
from sklearn.ensemble  import GradientBoostingClassifier as GBC, RandomForestClassifier
from sklearn.model_selection import GridSearchCV

1. Linear Regression:

2. Logistic Regression

3. Other regression problems

4. Classification and Regression Trees (Decision Tree)

5. Naive Bayes

6. Ensemble Models

  • Random Forest

  • Boosted Trees
    • Adaptive boosting
    • Gradient Boosting
      • Gradient boost
      • XGBoost
      • Catboost

7. Support Vector Machine (SVM)

8. K-nearest neigbour (KNN)