What are the three parts a categorical syllogismconsists of:
What are the three parts a categorical syllogismconsists of:
The best type of chart for comparing two sets of categorical data is:
The best type of chart for comparing two sets of categorical data is:
Nominal (categorical) data may be treated as ordinal or numerical (quantitative).
Nominal (categorical) data may be treated as ordinal or numerical (quantitative).
When analyzing categorical data, stem and leaf display is an appropriate plot.
When analyzing categorical data, stem and leaf display is an appropriate plot.
The two graphical techniques we usually use to present nominal (categorical) data are:
The two graphical techniques we usually use to present nominal (categorical) data are:
Name of Internet service provider is A: discrete numerical variable B: continuous numerical variable C: categorical variable
Name of Internet service provider is A: discrete numerical variable B: continuous numerical variable C: categorical variable
A categorical syllogism consists of two parts:major premise(All M are P),minor premise(All S are M).
A categorical syllogism consists of two parts:major premise(All M are P),minor premise(All S are M).
The classification of student class designation (freshman, sophomore, junior, senior) is an example of ( ) A: a categorical variable B: a discrete variable C: a continuous variable D: a parameter
The classification of student class designation (freshman, sophomore, junior, senior) is an example of ( ) A: a categorical variable B: a discrete variable C: a continuous variable D: a parameter
The classification of student class designation (freshman, sophomore, junior, senior) is an example of A: a categorical random variable. B: a discrete random variable. C: a continuous random variable. D: a parameter.
The classification of student class designation (freshman, sophomore, junior, senior) is an example of A: a categorical random variable. B: a discrete random variable. C: a continuous random variable. D: a parameter.
编译模型时用了以下代码:model.compile(optimizer=’Adam,loss=’categorical.crossentropy’,metrics=[tf.keras.metrics.accuracy]),在使用evaluate方法评估模型时,会输出以下哪些指标?() A: accuracy B: categorical_1oss C: loss D: categoricalaccuracy
编译模型时用了以下代码:model.compile(optimizer=’Adam,loss=’categorical.crossentropy’,metrics=[tf.keras.metrics.accuracy]),在使用evaluate方法评估模型时,会输出以下哪些指标?() A: accuracy B: categorical_1oss C: loss D: categoricalaccuracy