Machine learning course memo

Introduction [P01]

What is machine learning?

Technical research area that allows machines to learn their behavior from data

Differences between deep learning and traditional machine learning

Feature engineering was absorbed by the algorithm

Wave of democratization

(2)

Free lunch theorem The optimal algorithm depends on the data

Penalty term = harm

PCA: Principal component? ??

In the algorithm Regularization: holdout Before the algorithm Dimensionality reduction (feature extraction, feature selection) After the algorithm Cross-validation: kfold

StandardScaler() Subtract the mean and divide by the standard deviation

    from sklearn.model_selection import cross_val_score, KFold
# build models
kf = KFold(n_splits=3, shuffle=True, random_state=0)
for pipe_name, est in pipelines.items():
  cv_results = cross_val_score(est,
                                  X, y,
                                 cv=kf,
                                 scoring='r2')
    print('----------')
    print('algorithm:', pipe_name)
    print('cv_results:', cv_results)
    print('avg +- std_dev', cv_results.mean(),'+-', cv_results.std())

Supervised learning (regression) [P02]

Phase

To increase generalization ability

Regression algorithm

Dealing with overfitting

algorithm

Evaluation of regression model

Supervised learning (classification) [P03]

Classification algorithm

Classification model evaluation method

Data preprocessing and dimension reduction [P04]

Data preprocessing

Dimensionality reduction

  from sklearn.feature_selection import RFE
  selector = RFE(RandomForestRegressor(n_estimators=100, random_state=42), n_features_to_select=5)

Unsupervised learning [P06]

Clustering

Collect data with high similarity from data that has no correct answer k-means

Scheduled to be added

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