SmplML 1.0.5

SmplML is a user-friendly Python module for streamlined machine learning classification and regression. It offers intuitive functionality for data preprocessing, model training, and evaluation. Ideal for beginners and experts alike, SmplML simplifies ML tasks, enabling you to gain valuable insights from your data with ease.

SmplML / SimpleML: Simplified Machine Learning for Classification and Regression

SmplML is a user-friendly Python module for streamlined machine learning classification and regression. It offers intuitive functionality for data preprocessing, model training, and evaluation. Ideal for beginners and experts alike, SmplML simplifies ML tasks, enabling you to gain valuable insights from your data with ease.

Features

• Data preprocessing: Easily handle encoding categorical variables and data partitioning.
• Model training: Train various classification and regression models with just a few lines of code.
• Model evaluation: Evaluate model performance using common metrics and visualizations.
• This module is designed to seamlessly handle various scikit-learn models, making it flexible for handling sklearn-like model formats.

Installation

• You can install SmpML using pip:

pip install SimpleML

Usage

The TrainModel class is designed to handle both classification and regression tasks. It determines the task type based on the target parameter. If the target has a float data type, the class automatically redirects the procedures to regression; otherwise, it assumes a classification task.

Data preparation like data spliting and converting categorical data into numerical data is also automatically executed when calling the fit() method

Classification

import seaborn as sns
import pandas as pd
from smpl_ml.smpl_ml import TrainModel

from sklearn.neighbors import KNeighborsClassifier

# Load the dataset
df = sns.load_dataset('penguins')

# Set the target and features
target = 'species'
features = df.iloc[:, df.columns != target].columns # list of columns

# Create the classifier trainer
clf_trainer = TrainModel(df.dropna(), target=target, features=features, model=KNeighborsClassifier())

# Fit the model
clf_trainer.fit()

# Evaluate the model
clf_model = clf_trainer.evaluate()

model when verbose is set to True will return a DataFrame of metrics.

	Recall	Specificity	Precision	F1-Score	Accuracy
Adelie	0.91	0.70	0.67	0.77	0.76
Chinstrap	0.38	0.92	0.62	0.47	0.76
Gentoo	0.86	1.00	1.00	0.92	0.76

Regression

import seaborn as sns
import pandas as pd
from smpl_ml.smpl_ml import TrainModel

from sklearn.neighbors import KNeighborsRegressor

# Load the dataset
df = sns.load_dataset('iris')

# Set the target and features
target = 'sepal_length'
features = df.columns[1:] # list of columns

# Create the regressor trainer
reg_trainer = TrainModel(df.dropna(), target=target, features=features, model=KNeighborsRegressor())

# Fit the model
reg_trainer.fit()

# Evaluate the model
reg_model = reg_trainer.evaluate()

	Metrics
MSE	0.11
RMSE	0.34
MAE	0.27
R-Squared	0.74

Change Log

1.0.5 (06/12/2023)

• Added regression
• Modified docstrings
• Added pre-defined function
• Fixed local issues