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.
Project description
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.
- 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.
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
# 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:]
# 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
1.0.4 (06/12/2023)
Added Regression
Project details
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