[Kaggle Course] Introduction

Learn to handle missing values, non-numeric values, data leakage and more.

Your models will be more accurate and useful

 

 you will accelerate your machine learning expertise by learning how to:

• tackle data types often found in real-world datasets (missing values, categorical variables),
• design pipelines to improve the quality of your machine learning code,
• use advanced techniques for model validation (cross-validation),
• build state-of-the-art models that are widely used to win Kaggle competitions (XGBoost), and
• avoid common and important data science mistakes (leakage).

 

 

 

from sklearn.ensemble import RandomForestRegressor

# Define the models
model_1 = RandomForestRegressor(n_estimators=50, random_state=0)
model_2 = RandomForestRegressor(n_estimators=100, random_state=0)
model_3 = RandomForestRegressor(n_estimators=100, criterion='mae', random_state=0)
model_4 = RandomForestRegressor(n_estimators=200, min_samples_split=20, random_state=0)
model_5 = RandomForestRegressor(n_estimators=100, max_depth=7, random_state=0)

models = [model_1, model_2, model_3, model_4, model_5]

 

모델을 다양하게 만든 뒤 각각 MAE를 측정해보고 가장 낮은 MAE를 산출하는 모델을 고르자.