model = Sequential() model.class_mode="categorical"model._train = _train model._train_with_acc = _train_with_acc model._predict = _predict model._test = _test model._test_with_acc = _test_with_acc# Train the model each generation and show predictions against the validation datasetforite...
Factors: categorical varable属性变量 主要目的是用来分类,计算频数和频率 factor(x=character(),levels,labels=levels)gl(n,k,length=n*k,labels=1:n,ordered=FALSE)###n:水平数;k:每个因子重复次数;length:总长度(类似rep中each和times) > gl(3,2,18) [1] 1 1 2 2 3 3 1 1 2 2 3 3 1 1 ...
All models contained a combination of categorical and continuous indicators. All categorical indicators were dichotomous, and continuous indicators were normally distributed. The fit indices examined were Akaike's information criterion, Bayesian information criterion (BIC), sample size-adjusted Bayesian ...
All models contained a combination of categorical and continuous indicators. All categorical indicators were dichotomous, and continuous indicators were normally distributed. The fit indices examined were Akaike's information criterion, Bayesian information criterion (BIC), sample size-adjusted Bayesian ...