我正在编写一个机器学习代码（分类），用于区分两个类别。我开始时只为所有图像捕捉一个特征。
例如：（注意：1和0用于标记）class A=[(4295046.0, 1), (4998220.0, 1), (4565017.0, 1), (4078291.0, 1), (4350411.0, 1), (4434050.0, 1), (4201831.0, 1), (4203570.0, 1), (4197025.0, 1), (4110781.0, 1), (4080568.0, 1), (4276499.0, 1), (4363551.0, 1), (4241573.0, 1), (4455070.0, 1), (5682823.0, 1), (5572122.0, 1), (5382890.0, 1), (5217487.0, 1), (4714908.0, 1), (4697137.0, 1), (4057898.0, 1), (4143981.0, 1), (3899129.0, 1), (3830584.0, 1), (3557377.0, 1), (3125518.0, 1), (3197039.0, 1), (3109404.0, 1), (3024219.0, 1), (3066759.0, 1), (2726363.0, 1), (3507626.0, 1), …..等]
class B=[(7179088.0, 0), (7144249.0, 0), (6806806.0, 0), (5080876.0, 0), (5170390.0, 0), (5694876.0, 0), (6210510.0, 0), (5376014.0, 0), (6472171.0, 0), (7112956.0, 0), (7356507.0, 0), (9180030.0, 0), (9183460.0, 0), (9212517.0, 0), (9055663.0, 0), (9053709.0, 0), (9103067.0, 0), (8889903.0, 0), (8328604.0, 0), (8475442.0, 0), (8499221.0, 0), (8752169.0, 0), (8779133.0, 0), (8756789.0, 0), (8990732.0, 0), (9027381.0, 0), (9090035.0, 0), (9343846.0, 0), (9518609.0, 0), (9435149.0, 0), (9365842.0, 0), (9395256.0, 0), (4381880.0, 0), (4749338.0, 0), (5296143.0, 0), (5478942.0, 0), (5610865.0, 0), (5514997.0, 0), (5381010.0, 0), (5090416.0, 0), (4663958.0, 0), (4804526.0, 0), (4743107.0, 0), (4898914.0, 0), (5018503.0, 0), (5778240.0, 0), (5741893.0, 0), (4632926.0, 0), (5208486.0, 0), (5633403.0, 0), (5699410.0, 0), (5748260.0, 0), (5869260.0, 0), ….等]
/data 是 A 和 B 的组合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 RandomForestClassifierclf = RandomForestClassifier(n_estimators=100, max_depth=2, random_state=0)clf.fit(x_train, y_train)问题：
要添加另一个特征需要做哪些改动？在添加特征时，A和B应该是什么样子？我是否需要更改这一行
clf = RandomForestClassifier(n_estimators=100, max_depth=2, random_state=0)在使用两个特征时？
我的猜测：
class A=[(4295046.0,第二个特征, 1), (4998220.0,第二个特征, 1), (4565017.0,第二个特征, 1), (4078291.0,第二个特征, 1), (4350411.0,第二个特征, 1), (4434050.0, 1),……]这是对的吗？有没有更好的方法？
回答：
这个模型不需要明确指定特征的数量。
如果数据中的每个元组的最后一个元素始终是类别，你可以这样做：
x = [[each[:-1]] for each in data]y = [[each[-1]] for each in data]然后继续进行后续操作即可。
