codesnips

machine-learning

python 
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler
from sklearn.linear_model import LogisticRegression

standard_pipeline = Pipeline([
    ('scaler', StandardScaler()),

Scaling and normalization choices for different model families

feature-scaling normalization machine-learning
by Dr. Elena Vasquez 1 tab
python 
import pandas as pd

df = pd.read_parquet('churn_training.parquet')

print('shape:', df.shape)
print('target balance:', df['churned'].value_counts(normalize=True).round(3))

Exploratory data analysis checklist for tabular ML projects

eda machine-learning tabular-data
by Dr. Elena Vasquez 1 tab
python 
import optuna
from sklearn.ensemble import HistGradientBoostingClassifier
from sklearn.model_selection import cross_val_score

def objective(trial):
    model = HistGradientBoostingClassifier(

Bayesian optimization with Optuna for efficient model tuning

optuna hyperparameter-tuning optimization
by Dr. Elena Vasquez 1 tab
python 
import pandera as pa
from pandera.typing import Series

class ChurnTrainingSchema(pa.DataFrameModel):
    customer_id: Series[int] = pa.Field(unique=True)
    age: Series[int] = pa.Field(ge=18, le=100)

Data validation contracts with Pandera for pipeline reliability

pandera data-validation schema
by Dr. Elena Vasquez 1 tab
python 
from sklearn.ensemble import RandomForestClassifier, HistGradientBoostingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer

Baseline classifiers in scikit-learn for fast benchmark setting

scikit-learn classification baselines
by Dr. Elena Vasquez 1 tab