Suppose that you are given a dataset of 2300×12 dimension. From the data set, last column is your target variable. After splitting your data into train and test sets, calculate the shapes of trainx and testy splits, if test size is assigned as 0.25.
your answer for question 2 should look like (for instance: trainx=2300×12, testy= 2300)
The Correct Answer and Explanation is:
Answer:
trainx = 1725x11, testy = 575Explanation:
In this problem, you are given a dataset of shape 2300×12, meaning it contains 2300 rows (samples) and 12 columns (features + target variable). The last column (i.e., the 12th column) is the target variable (y), and the remaining 11 columns are the feature variables (X).
Step 1: Splitting Features and Target
- Features (X) will have shape 2300×11
- Target (y) will have shape 2300
Step 2: Train-Test Split
You are instructed to split the data using test size = 0.25. This means:
- 25% of data goes to the test set
- 75% of data goes to the training set
So, we compute the split as:
- 25% of 2300 = 575 samples → test set
- 75% of 2300 = 1725 samples → training set
Now, let’s define the resulting shapes:
trainx:
- Refers to the features (X) part of the training set.
- Since the training set has 1725 samples and there are 11 features:
- Shape of
trainx= 1725×11
testy:
- Refers to the target variable (y) part of the test set.
- Since the test set has 575 samples and the target is a single value per sample:
- Shape of
testy= 575
Why It Matters:
Understanding how to calculate shapes after a train-test split is crucial for:
- Model training: ML models take feature matrices
X_trainand predicty_train. - Model evaluation: We assess model performance using
X_testto predicty_test. - Ensuring shape compatibility avoids errors in training, prediction, and evaluation.
Correct shapes lead to successful data preparation, a critical first step in any machine learning pipeline.