Importance of Data Splitting
Splitting data is an essential part of training a supervised machine learning model. It ensures that the model is evaluated and tested properly to avoid biased results.
Supervised machine learning models rely on having sufficient labeled data to learn from. Since the model has access to labeled data, it needs a way to measure its accuracy.
Without accuracy measurement, it’s difficult to know if the model is learning or not. Data splitting ensures that the evaluation and testing of the model are unbiased.
Training, Validation, and Test Sets
At the heart of data splitting are the training, validation, and test sets. The training set is used to train the model, the validation set is used to tune the model’s hyperparameters, and the test set is used to assess the model’s performance.
Often, the split ratio between the training, validation, and test sets can vary based on the problem and the amount of data available.
Underfitting and Overfitting
Underfitting and overfitting are common problems when training machine learning models. Underfitting happens when the model is too simple and cannot capture the complexity of the data.
Overfitting happens when the model is too complex and over-generalizes the data. It’s important to balance these two states to ensure that the model has reasonable performance.
Prerequisites for Using train_test_split()
The train_test_split() function is part of the sklearn library and requires the NumPy library as a prerequisite. The function helps split a dataset into training and test sets.
The package is useful when splitting sequences of data. The sequence can be split randomly, shuffled, and stratified.
Application of train_test_split()
The train_test_split() function can be applied to a variety of use cases, including linear regression, classification, and other validation functionalities. In a minimalist example of linear regression, we first create a dataset, fit the model, and plot the results.
In a regression example, we use the Boston house prices dataset to predict house prices with a linear regression model. In a classification example, we use the iris dataset to classify different species of iris flowers based on their measurements.
Minimalist Example of Linear Regression
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
X = np.arange(0, 20, 1.0)
y = 2.0*X + 3.0 + np.random.normal(scale=3.0, size=X.shape)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
plt.scatter(X_train, y_train, alpha=0.7, label='Training Data')
plt.scatter(X_test, y_test, alpha=0.7, label='Test Data')
plt.legend()
plt.show()Regression Example
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
boston = load_boston()
X = boston.data
y = boston.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
lr = LinearRegression().fit(X_train, y_train)
y_train_pred = lr.predict(X_train)
y_test_pred = lr.predict(X_test)
print("nTraining MSE:", mean_squared_error(y_train, y_train_pred))
print("Testing MSE:", mean_squared_error(y_test, y_test_pred))Classification Example
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
iris = load_iris()
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
clf = DecisionTreeClassifier(max_depth=2).fit(X_train, y_train)
y_train_pred = clf.predict(X_train)
y_test_pred = clf.predict(X_test)
print("nTraining Accuracy:", accuracy_score(y_train, y_train_pred))
print("Testing Accuracy:", accuracy_score(y_test, y_test_pred))Other Validation Functionalities
KFold, StratifiedKFold, TimeSeriesSplit, GroupKFold, and ShuffleSplit are other validation functionalities within the sklearn library. These functions help to split data into more nuanced configurations, validate time-series data, and ensure compatibility for groups or stochastic methods like random forests.
Conclusion
Data splitting is a critical aspect of developing machine learning models that can adapt to new data and provide accurate predictions. The train_test_split() function is a vital tool in the field of machine learning that enables model creators to train, evaluate, and optimize models.
By learning how to use train_test_split() and understand its functionality, data scientists can develop more robust and scalable models that can meet the needs of complex datasets. In conclusion, data splitting is crucial for training machine learning models and evaluating their performance.
The use of train_test_split() in combination with other validation functionalities like KFold, StratifiedKFold, TimeSeriesSplit, GroupKFold, and ShuffleSplit ensures unbiased evaluation, reduces underfitting and overfitting risks, and provides accurate predictions. By using this function and other methods in the sklearn library, we can develop reliable models that can generalize to unknown data with confidence.
Therefore, it’s essential to understand the features and importance of data splitting to ensure that the analysis and modeling process generates results that reflect the actual predictions of a model rather than random errors and bias.