I have a dataset as below:
data = [[92, 155],
[56, 186, 117, 210, 224],
[247, 202, 189, 210, 65, 3, 270, 224],
[20, 14, 157, 224],
[17, 89, 158, 224],
[263, 283, 68, 224],
[182, 166, 224],
[176, 37, 100, 224],
[33, 102, 41, 269, 177, 224],
[0, 260, 49, 207, 278, 217, 35],
[119],
[118],
[142, 185, 7, 246, 224],
[104, 22, 101, 224],
[84, 205, 224],
[225, 93, 54, 224],
[98, 32, 78, 224],
[159, 217, 212, 198, 224],
[178, 94, 187, 224],
[211, 149, 193, 149, 66, 139, 67, 28, 106, 224],
[133, 151],
[259, 109, 29, 224],
[215, 241, 73, 255, 77, 144, 224],
[36, 254, 19, 268, 183, 224],
[47, 234, 203, 111, 231, 141, 30],
[127, 275, 220, 161],
[214, 267, 22, 90, 224],
[46, 217, 103],
[17, 89, 128, 224],
[225, 22, 101, 224],
[285, 265, 151],
[215, 206, 264, 43, 224],
[244, 21, 224],
[82, 122, 240, 5, 224],
[259, 136, 162, 194, 224],
[176, 208, 112, 224],
[172, 19, 146, 276, 31, 246, 51, 224],
[45, 10],
[229, 24, 224],
[143, 108, 239, 224],
[225, 282, 83, 224],
[110, 267, 171],
[176, 245, 95, 123, 270, 224],
[248, 195, 139, 261, 173, 281, 232, 80, 18, 224],
[61, 60, 233],
[211, 120, 1, 23],
[225, 267, 249, 224],
[247, 202, 86, 196, 224],
[15, 127, 222, 224],
[247, 202, 186, 226, 145, 224],
[174, 242, 196, 224],
[259, 152, 71, 224],
[235, 44, 230, 224],
[69, 96, 50, 99, 116],
[259, 279, 224],
[228, 70],
[39, 139, 201, 190, 224],
[132, 40, 219, 81, 224],
[159, 221, 224],
[267, 16, 6, 62],
[143, 59, 175, 129, 48, 224],
[280, 140, 224],
[284, 124, 167, 150, 274],
[113, 265, 184],
[179, 4, 257, 145, 224],
[247, 202, 72, 11, 224],
[64],
[192, 125, 105],
[174, 134, 224],
[58, 139, 85, 160, 209, 224],
[130, 169, 137, 256, 224],
[215, 163, 265, 185, 26],
[176, 147, 74, 224],
[0, 266],
[143, 34, 153, 188, 224],
[121],
[243, 75, 135],
[38, 218, 199, 253, 224],
[178, 271, 224],
[154, 164, 180, 27, 270, 224],
[176, 189, 148, 139, 277, 224],
[57, 62],
[91, 168, 251, 224],
[172, 19, 146, 276, 53, 97, 200, 224],
[64],
[8, 237, 224],
[138, 107, 224],
[176, 238, 224],
[204, 217, 63, 165, 224],
[215, 216, 272, 62, 170, 2, 55, 224],
[247, 273, 202, 223, 9, 148, 224],
[258, 267, 181, 224],
[262, 76, 126, 12, 224],
[36, 254, 19, 268, 250, 213, 48, 224],
[227, 42],
[79, 197, 52, 87, 224],
[143, 131, 224],
[156, 88, 115, 236, 224],
[259, 13, 252, 224],
[114, 25, 191, 224]]
target = [9,
31,
20,
9,
3,
26,
16,
11,
28,
0,
9,
9,
9,
9,
7,
1,
33,
9,
13,
15,
9,
21,
9,
34,
9,
9,
9,
9,
3,
1,
9,
27,
14,
22,
21,
11,
17,
9,
6,
8,
1,
9,
11,
9,
9,
9,
1,
20,
29,
20,
23,
21,
9,
9,
21,
9,
18,
9,
9,
30,
8,
9,
9,
9,
9,
20,
9,
32,
23,
9,
24,
9,
11,
9,
8,
9,
9,
9,
13,
10,
11,
9,
12,
17,
9,
5,
9,
11,
9,
2,
20,
9,
25,
34,
9,
9,
8,
4,
21,
19]
I wish to do classification by using naive bayes or any other BEST algorithms available. However, I received error when using naive bayes as below:
from sklearn.naive_bayes import MultinomialNB
mnb = MultinomialNB(class_prior=[.25,.75])
mnb.fit(data, target)
Errros:
ValueError: Expected 2D array, got 1D array instead:
array=[list([92, 155]) list([56, 186, 117, 210, 224])
list([247, 202, 189, 210, 65, 3, 270, 224]) list([20, 14, 157, 224])
list([17, 89, 158, 224]) list([263, 283, 68, 224]) list([182, 166, 224])
list([176, 37, 100, 224]) list([33, 102, 41, 269, 177, 224])
list([0, 260, 49, 207, 278, 217, 35]) list([119]) list([118])
list([142, 185, 7, 246, 224]) list([104, 22, 101, 224])
list([84, 205, 224]) list([225, 93, 54, 224]) list([98, 32, 78, 224])
list([159, 217, 212, 198, 224]) list([178, 94, 187, 224])
list([211, 149, 193, 149, 66, 139, 67, 28, 106, 224]) list([133, 151])
list([259, 109, 29, 224]) list([215, 241, 73, 255, 77, 144, 224])
list([36, 254, 19, 268, 183, 224])
list([47, 234, 203, 111, 231, 141, 30]) list([127, 275, 220, 161])
list([214, 267, 22, 90, 224]) list([46, 217, 103])
list([17, 89, 128, 224]) list([225, 22, 101, 224]) list([285, 265, 151])
list([215, 206, 264, 43, 224]) list([244, 21, 224])
list([82, 122, 240, 5, 224]) list([259, 136, 162, 194, 224])
list([176, 208, 112, 224]) list([172, 19, 146, 276, 31, 246, 51, 224])
list([45, 10]) list([229, 24, 224]) list([143, 108, 239, 224])
list([225, 282, 83, 224]) list([110, 267, 171])
list([176, 245, 95, 123, 270, 224])
list([248, 195, 139, 261, 173, 281, 232, 80, 18, 224])
list([61, 60, 233]) list([211, 120, 1, 23]) list([225, 267, 249, 224])
list([247, 202, 86, 196, 224]) list([15, 127, 222, 224])
list([247, 202, 186, 226, 145, 224]) list([174, 242, 196, 224])
list([259, 152, 71, 224]) list([235, 44, 230, 224])
list([69, 96, 50, 99, 116]) list([259, 279, 224]) list([228, 70])
list([39, 139, 201, 190, 224]) list([132, 40, 219, 81, 224])
list([159, 221, 224]) list([267, 16, 6, 62])
list([143, 59, 175, 129, 48, 224]) list([280, 140, 224])
list([284, 124, 167, 150, 274]) list([113, 265, 184])
list([179, 4, 257, 145, 224]) list([247, 202, 72, 11, 224]) list([64])
list([192, 125, 105]) list([174, 134, 224])
list([58, 139, 85, 160, 209, 224]) list([130, 169, 137, 256, 224])
list([215, 163, 265, 185, 26]) list([176, 147, 74, 224]) list([0, 266])
list([143, 34, 153, 188, 224]) list([121]) list([243, 75, 135])
list([38, 218, 199, 253, 224]) list([178, 271, 224])
list([154, 164, 180, 27, 270, 224]) list([176, 189, 148, 139, 277, 224])
list([57, 62]) list([91, 168, 251, 224])
list([172, 19, 146, 276, 53, 97, 200, 224]) list([64])
list([8, 237, 224]) list([138, 107, 224]) list([176, 238, 224])
list([204, 217, 63, 165, 224]) list([215, 216, 272, 62, 170, 2, 55, 224])
list([247, 273, 202, 223, 9, 148, 224]) list([258, 267, 181, 224])
list([262, 76, 126, 12, 224]) list([36, 254, 19, 268, 250, 213, 48, 224])
list([227, 42]) list([79, 197, 52, 87, 224]) list([143, 131, 224])
list([156, 88, 115, 236, 224]) list([259, 13, 252, 224])
list([114, 25, 191, 224])].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.
Please can anyone please help me with this? Or can someone show me some other best examples of algorithms to use for machine learning like decision tree, svm or anything.
So the direct answer to why you're getting an error is that you're passing an array of lists as an argument. Sklearn thus think you're passing in a 1D array of lists. It is not possible to transform your data to a 2D matrix because the number of values in your list in inconsistent.
From my understanding (which could be wrong), each row of your input feature matrix need to have the same amount of numbers. Given this is satisfied, then you should be able to feed your data into MultinomialNB no problem.
Consider padding with zeros:
data1 = np.zeros((len(data), 10))
for i in range(len(data)):
data1[i, :len(data[i])] = data[i]