Overview

This section provides a simple example to get started with classification problem.

Example

The arrowhead data consists of outlines of the images of arrowheads. The shapes of the projectile points are converted into a time series using the angle-based method. The classification of projectile points is an important topic in anthropology. The classes are based on shape distinctions such as the presence and location of a notch in the arrow. The problem in the repository is a length normalised version of that used in Ye09shapelets. The three classes are called “Avonlea”, “Clovis” and “Mix”.”

Dataset details: here. Dimensionality: univariate Series length: 251 Train cases: 36 Test cases: 175 Number of classes: 3.

from sktime.classification.interval_based import TimeSeriesForestClassifier
from sktime.datasets import load_arrow_head
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

X, y = load_arrow_head()
X_train, X_test, y_train, y_test = train_test_split(X, y)

classifier = TimeSeriesForestClassifier()
classifier.fit(X_train, y_train)

y_pred = classifier.predict(X_test)
accuracy_score(y_test, y_pred)

0.8867924528301887