The Wine Line-Up — medium Machine Learning in Code problem

The Wine Line-Up

Run six classifiers side by side on the wine data, the same way the lesson did for breast cancer.

Write wine_line_up(seed).

Load the data with load_wine(return_X_y=True, as_frame=True) and split it with train_test_split(X, y, test_size=0.25, random_state=seed, stratify=y).
Build these models, under these names:
- "logistic": make_pipeline(StandardScaler(), LogisticRegression(max_iter=1000))
- "knn": make_pipeline(StandardScaler(), KNeighborsClassifier(n_neighbors=5))
- "bayes": GaussianNB()
- "svm": make_pipeline(StandardScaler(), SVC(kernel="linear"))
- "tree": DecisionTreeClassifier(max_depth=3, random_state=0)
- "forest": RandomForestClassifier(n_estimators=100, random_state=0)
Fit each on the training rows and score it on the test rows.

Return a dict with the six names mapped to their test scores, rounded to 3 places, plus one more entry "best": the name with the highest rounded score. On a tie, pick the name that comes first in the list above.

Examples

seed = 0

returns {'logistic': 1.0, 'knn': 0.933, 'bayes': 0.956, 'svm': 0.978, 'tree': 0.822, 'forest': 1.0, 'best': 'logistic'}

seed = 1

returns {'logistic': 0.978, 'knn': 0.933, 'bayes': 1.0, 'svm': 0.956, 'tree': 0.933, 'forest': 1.0, 'best': 'bayes'}

Hints

1from sklearn.datasets import load_wine

2from sklearn.ensemble import RandomForestClassifier

3from sklearn.linear_model import LogisticRegression

4from sklearn.model_selection import train_test_split

5from sklearn.naive_bayes import GaussianNB

6from sklearn.neighbors import KNeighborsClassifier

7from sklearn.pipeline import make_pipeline

8from sklearn.preprocessing import StandardScaler

9from sklearn.svm import SVC

10from sklearn.tree import DecisionTreeClassifier

13def wine_line_up(seed):

14 pass