Evaluating multiple models

Supervised Learning with scikit-learn

George Boorman

Core Curriculum Manager, DataCamp

Different models for different problems

Some guiding principles

Size of the dataset
- Fewer features = simpler model, faster training time
- Some models require large amounts of data to perform well
Interpretability
- Some models are easier to explain, which can be important for stakeholders
- Linear regression has high interpretability, as we can understand the coefficients
Flexibility
- May improve accuracy, by making fewer assumptions about data
- KNN is a more flexible model, doesn't assume any linear relationships

It's all in the metrics

Regression model performance:
- RMSE
- R-squared
Classification model performance:
- Accuracy
- Confusion matrix
- Precision, recall, F1-score
- ROC AUC
Train several models and evaluate performance out of the box

A note on scaling

Models affected by scaling:
- KNN
- Linear Regression (plus Ridge, Lasso)
- Logistic Regression
- Artificial Neural Network

Best to scale our data before evaluating models

Evaluating classification models

import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import cross_val_score, KFold, train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier

X = music.drop("genre", axis=1).values
y = music["genre"].values
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)

X_test_scaled = scaler.transform(X_test)

Evaluating classification models

models = {"Logistic Regression": LogisticRegression(), "KNN": KNeighborsClassifier(), 
         "Decision Tree": DecisionTreeClassifier()}
results = []

for model in models.values():

     kf = KFold(n_splits=6, random_state=42, shuffle=True)

     cv_results = cross_val_score(model, X_train_scaled, y_train, cv=kf)

     results.append(cv_results)

plt.boxplot(results, labels=models.keys())
plt.show()

Visualizing results

Box plot of accuracy for each model: Logistic Regression, KNN, and Decision Tree

Test set performance

for name, model in models.items():

  model.fit(X_train_scaled, y_train)

  test_score = model.score(X_test_scaled, y_test)

  print("{} Test Set Accuracy: {}".format(name, test_score))

Logistic Regression Test Set Accuracy: 0.844
KNN Test Set Accuracy: 0.82
Decision Tree Test Set Accuracy: 0.832

Let's practice!

Supervised Learning with scikit-learn