Consider the following Python code:

X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42
)

If the original dataset contains 1,000 observations, how many will be in the training set?

Question

Consider the following Python code:

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

If the original dataset contains 1,000 observations, how many will be in the training set?

BlackTom AI · Accepted Answer

train_test_split with test_size=0.2 reserves 20% of the data for the test set, so the remaining 80% becomes the training set.

Relevant concepts:
- test_size parameter: fraction (or absolute number) of the original dataset allocated to the test split. Here test_size=0.2 means 20% for testing.
- Complement for training: training fraction = 1 - test_size = 0.8 (i.e., 80%).
- random_state: a seed for reproducible shuffling; it does not change the split proportions or counts, only the random order.

Step-by-step reasoning and calculations:
1. Total observations N = 1,000.
2. Compute test set size: N_test = test_size * N = 0.2 * 1,000.
   - Intermediate step: 0.2 * 1,000 = 200.
   - So N_test = 200 observations.
3. Compute training set size either by subtraction or by direct multiplication:
   - By subtraction: N_train = N - N_test = 1,000 - 200 = 800.
   - Or directly: N_train = (1 - test_size) * N = 0.8 * 1,000 = 800.
4. Result: training set contains 800 observations.

Why the correct answer is correct:
Because test_size=0.2 explicitly sets aside 20% of 1,000 observations (200) for testing; the remaining 80% (800) are assigned to training. The function returns X_train, X_test, y_train, y_test in that order, matching these counts for features and labels.

Why the other multiple-choice options are incorrect:
- 1000: would mean no split occurred; contradicts test_size=0.2 which reserves some data for testing.
- 200: this is the size of the test set, not the training set.
- 42: this is the random_state seed, not a count of observations; random_state only controls reproducibility.

Conclusion: The training set contains 800 observations, matching the provided correct answer "800".

Consider the following Python code: X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=42 ) If the original dataset contains 1,000 observations, how many will be in the training set?单项选择题

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