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Summary - Pipeline

In this group you learned the Pipeline structure and advantages.

Summary: pipelines

In this group:

  • A pipeline makes preprocessing and the model one estimator; it prevents leakage structurally, especially for steps that look at the target.
  • Intermediate steps must be transformers; steps are reached with pipe["name"], pipe[:-1]; "passthrough" turns a step off.
  • fit_transform runs in fit, transform in predict.
  • With joblib the whole chain is saved and moved to production.
  • ColumnTransformer applies different transformations to different column groups.

Practice End-to-end chain

For the mixed table, build a make_pipeline from a ColumnTransformer (numbers with StandardScaler, city with OneHotEncoder(handle_unknown="ignore")) and LogisticRegression(), and train it with y. Print the prediction for a new row containing the city "Antalya", unseen in training.

Starter code

model.py
import numpy as np
import pandas as pd
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder

df = pd.DataFrame({
    "yas": [25, 32, 47, 51],
    "gelir": [3000, 4500, 8000, 6000],
    "sehir": ["Ankara", "İzmir", "Ankara", "Bursa"],
})
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import LogisticRegression

y = np.array([0, 0, 1, 1])
yeni = pd.DataFrame({"yas": [50], "gelir": [7500], "sehir": ["Antalya"]})

# buraya yaz

Sample solution

model.py
import numpy as np
import pandas as pd
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder

df = pd.DataFrame({
    "yas": [25, 32, 47, 51],
    "gelir": [3000, 4500, 8000, 6000],
    "sehir": ["Ankara", "İzmir", "Ankara", "Bursa"],
})
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import LogisticRegression

y = np.array([0, 0, 1, 1])
yeni = pd.DataFrame({"yas": [50], "gelir": [7500], "sehir": ["Antalya"]})

ct = ColumnTransformer([("sayi", StandardScaler(), ["yas", "gelir"]), ("kat", OneHotEncoder(handle_unknown="ignore"), ["sehir"])])
model = make_pipeline(ct, LogisticRegression()).fit(df, y)
print(model.predict(yeni))

Expected output

[1]

Questions and answers

Review the lesson questions together with their answers. To test yourself, solve the lesson interactively.

1 How is the <code>gamma</code> parameter of an SVC inside a pipeline written in a grid (step name "svc")?

  • "svc_gamma"
  • "svc__gamma" Correct answer
  • "gamma"
  • "svc.gamma"

Explanation: The step name and the parameter name are separated by a double underscore.

2 Combine preprocessing and the model with automatic names.

Question 
model = ____(ct, LogisticRegression())
Answer 
model = make_pipeline(ct, LogisticRegression())

Explanation: With make_pipeline the names become "columntransformer", "logisticregression".

3 Match the tools.

Term Meaning
Pipeline Sequential steps
ColumnTransformer Parallel column groups
joblib Saving
passthrough Turning a step off

All lessons in Intermediate

1 scikit-learn basics

  1. What is scikit-learn?
  2. Estimator API
  3. The fit method
  4. The predict method
  5. The score method
  6. Summary - scikit-learn

2 Pipeline

  1. What is a Pipeline?
  2. Steps
  3. fit and predict
  4. Advantages
  5. ColumnTransformer
  6. Summary - Pipeline

3 Cross-validation

  1. What is cross-validation?
  2. K-fold
  3. cross_val_score
  4. Stratified
  5. Advantages
  6. Summary - Cross-validation

4 Grid search

  1. What is grid search?
  2. GridSearchCV
  3. param_grid
  4. best_params_
  5. best_score_
  6. Summary - Grid search

5 SVM

  1. What is SVM?
  2. Margin
  3. Kernel
  4. C parameter
  5. Classification
  6. Summary - SVM

6 Project: SVM

  1. Preparing data
  2. Training model
  3. Prediction
  4. Evaluation

7 Random forest

  1. What is random forest?
  2. Ensemble
  3. n_estimators
  4. Bagging
  5. Feature importance
  6. Summary - Random forest

8 Boosting

  1. What is boosting?
  2. AdaBoost
  3. Gradient boosting
  4. Learning rate
  5. Advantages
  6. Summary - Boosting

9 Clustering

  1. What is clustering?
  2. KMeans
  3. Centroid
  4. Elbow method
  5. Labels
  6. Summary - Clustering

10 KMeans in depth

  1. KMeans algorithm
  2. Initialization
  3. n_init
  4. inertia_
  5. random_state
  6. Summary - KMeans

11 Hierarchical clustering

  1. What is hierarchical clustering?
  2. Dendrogram
  3. Agglomerative
  4. Linkage
  5. Advantages
  6. Summary - Hierarchical clustering

12 Project: Clustering

  1. Preparing data
  2. Training KMeans
  3. Label analysis
  4. Printing results

13 PCA

  1. What is PCA?
  2. Variance
  3. n_components
  4. fit_transform
  5. explained_variance_
  6. Summary - PCA

14 Dimensionality reduction

  1. What is dimensionality reduction?
  2. Why reduce?
  3. PCA vs t-SNE
  4. Visualization
  5. Advantages
  6. Summary - Dimensionality reduction

15 Feature selection

  1. What is feature selection?
  2. Variance threshold
  3. SelectKBest
  4. Recursive elimination
  5. Feature importance
  6. Summary - Feature selection

16 Imbalanced data

  1. What is imbalanced data?
  2. SMOTE
  3. Class weight
  4. Oversampling
  5. Metric selection
  6. Summary - Imbalanced data

17 Project: Features

  1. Feature extraction
  2. Feature selection
  3. Training model
  4. Evaluation

18 Ensemble methods

  1. What is ensemble?
  2. Voting
  3. Bagging
  4. Boosting
  5. Stacking
  6. Summary - Ensemble

19 Regularization

  1. What is regularization?
  2. L1 (Lasso)
  3. L2 (Ridge)
  4. alpha
  5. ElasticNet
  6. Summary - Regularization

20 Intermediate final

  1. Challenge 1
  2. Challenge 2
  3. Challenge 3
  4. Challenge 4