softauto 0.4.0

One-call EDA + preprocessing + feature selection + plotting + advisor + tuning (robust CV).

🪶 softauto — AutoML with seatbelts

AutoML that respects small, messy, real-world datasets.
One line in → a trained pipeline, metrics, and a human-readable report out.

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✨ Why softauto?

Most AutoML libraries brute-force models until something sticks.
softauto is different:

• 🧑‍🏫 Advisor mode – if your model underperforms, it suggests fixes (or auto-switches).
• ⚖️ Safe imbalance handling – SMOTE applied only when statistically valid, else falls back to class weights.
• 🧹 Robust preprocessing – rare category binning, outlier clipping, flexible scalers.
• 🔍 Smart feature selection – Mutual Information & RFECV options.
• 📊 Automatic reporting – HTML + plots (target distribution, missingness, correlations, feature importances).
• 🎯 Opinionated model zoo – Random Forest, GB, SVM, KNN, MLP, Ridge/Lasso, all with tuned search spaces.

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🚀 Quickstart

import pandas as pd
from softauto.autorun import AutoRun

# load your dataset
df = pd.read_csv("mydata.csv")

# run softauto
run = AutoRun(df=df, target="label", task="classification")
results = run.fit()

print(results["metrics"])        # final holdout metrics
print(results["best_model_name"])# chosen model
print(results["artifacts_dir"])  # directory with reports + plots