如何控制特征的数量[机器学习]？

我正在编写此机器学习代码（分类）以在两个类之间进行分类。我首先使用一项功能来捕获我的所有图像。

例如：（注：1 & 0 用于标注）class A=[(4295046.0, 1), (4998220.0, 1), (4565017.0, 1), (4078291.0, 1), (4350411.0, 1), (4.0) 1), (4201831.0, 1), (4203570.0, 1), (4197025.0, 1), (4110781.0, 1), (4080568.0, 1), (4276499.0, 1), (4), 5,17.3), (4), 5,13. , (4455070.0, 1), (5682823.0, 1), (5572122.0, 1), (5382890.0, 1), (5217487.0, 1), (4714908.0, 1), (7,8,10.0, 1), (46, 10) 4143981.0, 1), (3899129.0, 1), (3830584.0, 1), (3557377.0, 1), (3125518.0, 1), (3197039.0, 1), (3109.5) (3109.0, 10) (3109.0, 10), (3109.0, 10) 1), (2726363.0, 1), (3507626.0, 1), .....etc]

B类=[(7179088.0, 0), (7144249.0, 0), (6806806.0, 0), (5080876.0, 0), (5170390.0, 0), (5694876.0, 0), (6), 7,02, (6), 5, 02 ), (6472171.0, 0), (7112956.0, 0), (7356507.0, 0), (9180030.0, 0), (9183460.0, 0), (9212517.0, 0), (9212517.0, 0), (3), 5, 905 (905) (9103067.0, 0), (8889903.0, 0), (8328604.0, 0), (8475442.0, 0), (8499221.0, 0), (8752169.0, 0), (87,89,709) (87,890.70) , 0), (9027381.0, 0), (9090035.0, 0), (9343846.0, 0), (9518609.0, 0), (9435149.0, 0), (9365842.0, 0.5), (9), 86, 0.5 ), (4749338.0, 0), (5296143.0, 0), (5478942.0, 0), (5610865.0, 0), (5514997.0, 0), (5381010.0, 0), (5478942.0, 0), (5610865.0, 0), (5381010.0, 0), (6), 6.09. (4804526.0, 0), (4743107.0, 0), (4898914.0, 0), (5018503.0, 0), (5778240.0, 0), (5741893.0, 0), (46,507.0) (46,503.0) (46,503.0) (46,520.0) , 0), (5699410.0, 0), (5748260.0, 0), (5869260.0, 0), ...。等等]

/data is A and B combined

x = [[each[0]] for each in data]
y = [[each[1]] for each in data]
print (len(x), len(y))

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, 
random_state=42)
print (len(x_train), len(x_test))
print (len(y_train), len(y_test))

from sklearn.ensemble import RandomForestClassifier

clf = RandomForestClassifier(n_estimators=100, max_depth=2, random_state=0)
clf.fit(x_train, y_train)

问题：

更改什么以添加另一个功能？添加功能时 A 和 B 应该如何显示，我是否更改此行

clf = RandomForestClassifier(n_estimators=100, max_depth=2, random_state=0)

何时使用两个功能？

我猜：

A类=[(4295046.0,secons features, 1), (4998220.0,secons features, 1), (4565017.0,secons features, 1), (4078291.0,secons features, 1), (4350411.0,secons features, 1) 4434050.0, 1),......] 是吗？有更好的方法吗？