Understand the idea
Explained variance ratios measure variation along fitted axes relative to total variation in the prepared inputs. They say nothing directly about predicting a target.
Python skill: Reads each component’s share of input variance in fitted order; these values are not prediction scores.
Meet the syntax
pca.explained_variance_ratio_pca.explained_variance_ratio_- Reads each component’s share of input variance in fitted order; these values are not prediction scores.
Follow the code
Use the numbered comments to connect each Python block to the workflow above.
answer=pca.explained_variance_ratio_
This practice: Read and run the Python. Next: Change · Explained variance is not predictive accuracy.
Given data · PCA48
48 observations. Deterministic teaching observations; values illustrate the concept rather than a real population claim. The dataframe df is supplied afresh for each Run.
Reference labels are omitted from this preview and must remain outside fitting.
| a | b | c | d | e |
|---|---|---|---|---|
| 103.047 | 50.8622 | 22.7157 | 44.6184 | 0.906993 |
| 89.6002 | 44.3015 | 20.2703 | 40.1253 | -0.680116 |
| 107.505 | 53.9521 | 21.1565 | 41.7009 | 0.818161 |
| 109.406 | 54.2501 | 22.5252 | 44.9095 | 1.39291 |
| 80.4896 | 40.0557 | 14.1714 | 29.4087 | -3.27953 |
| 86.9782 | 44.1387 | 18.7213 | 37.5997 | -1.70077 |
| 101.278 | 50.4611 | 18.1185 | 36.0784 | -0.343557 |
| 96.8376 | 48.7875 | 17.4445 | 33.8533 | -0.987809 |
Column meanings and units
Column names describe the supplied features and target. Keep the stated units and row identities when making comparisons.
Synthetic data are deliberately small and reproducible. Their patterns illustrate an idea; they are not evidence about a real population.
| Column | Stored type |
|---|---|
| a | float64 |
| b | float64 |
| c | float64 |
| d | float64 |
| e | float64 |
Supplied setup · available if you need to inspect it
This code runs before your editor on every Run. These are the objects your exercise uses.
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
X=df.copy()
scaler=StandardScaler()
scaled=scaler.fit_transform(X)
pca=PCA().fit(scaled)
scores=pca.transform(scaled)
Your task · Follow
Inspect the explained variance ratios.
Hint 1 — Think
Variance ratios refer to the representation on which PCA was fitted.
Hint 2 — Tools
explained_variance_ratio_.
Hint 3 — Approach
Read the fitted per-component ratio array in component order.
Explained solution
answer=pca.explained_variance_ratio_
The ratios describe how input variation is distributed across axes; they are not prediction scores.
Helpful prior knowledge: Fit a reusable PCA representation These links are guidance, not locks.
Sources and API context
Examples run with this Playground’s scikit-learn 1.4.2 / Pyodide 0.26.4 runtime.