func TestMeanSquared(t *testing.T) {
X, y := datasets.Load("cancer")
tree, _ := DecisionTree(4, GINI)
tree.Fit(X, y)
yPred := tree.Classify(X)
err := metrics.MeanSquaredError(yPred, y)
fmt.Printf("cancer mean sq: %.3f\n", err)
}
func TestCancer(t *testing.T) {
X, y := datasets.Load("cancer")
datasets.RandomShuffle(X, y)
XTrain, XTest := X[:67], X[67:]
yTrain, yTest := y[:67], y[67:]
beta := LinearRegression(XTrain, yTrain)
// validate on held out data
yPred := matrix.VecMult(XTest, beta)
fmt.Println("cancer error", metrics.MeanSquaredError(yPred, yTest))
}
func TestPrint(t *testing.T) {
X, y := datasets.Load("cancer")
tree, _ := DecisionTree(4, GINI)
tree.Fit(X, y)
fmt.Println(tree)
}
func TestBreastCancer(t *testing.T) {
X, y := datasets.Load("cancer")
perf := testDataset(X, y)
fmt.Printf("Breast Cancer: %.3f\n", perf)
}
func TestWithIris(t *testing.T) {
X, y := datasets.Load("iris")
perf := testDataset(X, y)
fmt.Printf("Iris: %.3f\n", perf)
}