Minimal implementations of classical ML algorithms
Project description
Revisiting Classsical ML
Implementations of classical ML algorithms in Numpy. Algorithms covered:
- Linear Regression
- Logistic Regression
- Support Vector Machines
- K Nearest Neighbours (both classifier and regressor)
- Naive Bayes
- K Means Clustering
- Decision Trees
- HMM
Installation
pip install RCML
Import classical ML algorithm implementations
# Classification models
from RCML import KNN_Classifier
from RCML import Decision_Tree
from RCML import Logistic_Regression
from RCML import SVM
from RCML import Naive_Bayes
# Clustering models
from RCML import KMeans
from RCML import KMeansPlusPlus
# Regression models
from RCML import KNN_Regressor
from RCML import Linear_Regressor
# Sequence models
from RCML import viterbi as HMM
See examples of usage in the repo in files having prefix run_*
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