update classification rand tests

test/classification/low_precision.jl

@@ -5,7 +5,7 @@ Random.seed!(16)
 
 n,m = 10^3, 5;
 features = Array{Any}(undef, n, m);
-features[:,:] = randn(n, m);
+features[:,:] = rand(n, m);
 features[:,1] = round.(Int32, features[:,1]); # convert a column of 32bit integers
 weights = rand(-1:1,m);
 labels = round.(Int32, features * weights);
@@ -51,7 +51,7 @@ model, coeffs = build_adaboost_stumps(labels, features, n_iterations);
 preds = apply_adaboost_stumps(model, coeffs, features);
 cm = confusion_matrix(labels, preds)
 @test typeof(preds) == Vector{Int32}
-@test cm.accuracy > 0.2
+@test cm.accuracy > 0.7
 
 println("\n##### nfoldCV Classification Tree #####")
 n_folds             = Int32(3)
@@ -88,12 +88,12 @@ accuracy = nfoldCV_forest(
         min_samples_leaf,
         min_samples_split,
         min_purity_increase)
-@test mean(accuracy) > 0.6
+@test mean(accuracy) > 0.7
 
 println("\n##### nfoldCV Adaboosted Stumps #####")
 n_iterations        = Int32(15)
 accuracy = nfoldCV_stumps(labels, features, n_folds, n_iterations)
-@test mean(accuracy) > 0.1
+@test mean(accuracy) > 0.7
 
 
 # Test Int8 labels, and Float16 features

test/classification/random.jl

@@ -120,11 +120,11 @@ cm = confusion_matrix(labels, preds)
 println("\n##### nfoldCV Classification Tree #####")
 nfolds          = 3
 pruning_purity  = 1.0
-max_depth       = 3
+max_depth       = 5
 accuracy  = nfoldCV_tree(labels, features, nfolds, pruning_purity, max_depth; rng=10, verbose=false)
 accuracy2 = nfoldCV_tree(labels, features, nfolds, pruning_purity, max_depth; rng=10)
 accuracy3 = nfoldCV_tree(labels, features, nfolds, pruning_purity, max_depth; rng=5)
-@test mean(accuracy) > 0.6
+@test mean(accuracy) > 0.7
 @test accuracy == accuracy2
 @test accuracy != accuracy3