# Binary Classification Machine Learning Low Accuracy On Certain Datasets

I’m currently working on trying to get a program that takes in txt file with 300,000 lines, each of which has an input of 12 decimal numbers and a singular value 0 or 1 at the end, to hopefully be able to guess a different set’s value of either 0 or 1 based off 12 decimal numbers in front of it, but first I’d like the program to take a subsection of the train set and use it as a validation set so I could get a rough idea on what the accuracy is, which I’ve completed below in Pytorch.

I’ve been following a set of very useful tutorials online: (https://www.youtube.com/watch?v=OGpQxIkR4ao&ab_channel=PythonEngineer) and (https://www.youtube.com/watch?v=PXOzkkB5eH0&ab_channel=PythonEngineer)

for which while using the sample online set seemed to work perfectly providing an accuracy of 0.9+ but when I tried to use a local dataset on the program its accuracy seems to stay at roughly 0.5 which isn’t much better than random guessing (I go into further detail on this below the main code and main output). Changing properties like the epochs and learning rate does seem to have a minor effect on the accuracy so I’m assuming it’s not just guessing either completely 0’s or 1’s but the accuracy won’t change much more than 4% even with a large number of epochs and a very low learning rate.

So if anyone has any advice on what I could be doing wrong with this program it’d be greatly appricated, I think it’s a small mistake that I just can’t see as a similar version of the code was used on a seperate online data set (with a different data structure) and it did really well. Here is the code, output and a sample set of inputs attached:

Python Code:

``````#Setup
import torch
import torch.nn as nn
import torchvision
import numpy as np
import math

from sklearn import datasets
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split

class BoneDataset(Dataset):
def __init__(self):
xy = np.loadtxt('train-io.txt', delimiter=" ", dtype=np.float32, skiprows=0)
self.x = torch.from_numpy(xy[:, 0:12])
self.y = torch.from_numpy(xy[:, 12])
self.y = self.y.int()
self.xyn_samples, self.xyn_features = xy.shape
self.n_samples, self.n_features = self.x.shape

def __getitem__(self,index):
return self.x[index], self.y[index]

def __len__(self):
return self.n_samples

dataset = BoneDataset()

xt = dataset.x
yt = dataset.y
x = np.array(xt)
y = np.array(yt)
n_samples = dataset.n_samples
n_features = dataset.n_features

#x = bc.data
#y = bc.target
#n_samples, n_features = x.shape

print(x)
print(y)
print(x.shape)
print(y.shape)
print(type(x))
print(type(y))
print(n_samples, n_features)

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=1234)

scale = StandardScaler()
x_train = scale.fit_transform(x_train)
x_test = scale.transform(x_test)

x_train = torch.from_numpy(x_train.astype(np.float32))
x_test = torch.from_numpy(x_test.astype(np.float32))
y_train = torch.from_numpy(y_train.astype(np.float32))
y_test = torch.from_numpy(y_test.astype(np.float32))

y_train = y_train.view(y_train.shape[0], 1)
y_test = y_test.view(y_test.shape[0], 1)

#Model
class LogisticRegression(nn.Module):
def __init__(self, n_input_features):
super(LogisticRegression, self).__init__()
self.linear = nn.Linear(n_input_features, 1)

def forward(self, x):
y_predicted = torch.sigmoid(self.linear(x))
return y_predicted

model = LogisticRegression(n_features)

#Loss And Optimizer
num_epochs = 100
learning_rate = 0.01
criterion = nn.BCELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)

#Training Loop
for epoch in range(num_epochs):
y_predicted = model(x_train)
loss = criterion(y_predicted, y_train)

loss.backward()
optimizer.step()

if(epoch+1) % 10 == 0:
print(f'epoch: {epoch+1}, loss = {loss.item():.4f}')

y_pred = model(x_test)
y_pred_cls= y_pred.round()
acc = y_pred_cls.eq(y_test).sum() / float(y_test.shape[0])
print(f'accuracy: {acc.item():.4f}')
``````

Output:

``````epoch: 10, loss = 0.7625
epoch: 20, loss = 0.7567
epoch: 30, loss = 0.7514
epoch: 40, loss = 0.7464
epoch: 50, loss = 0.7419
epoch: 60, loss = 0.7377
epoch: 70, loss = 0.7339
epoch: 80, loss = 0.7304
epoch: 90, loss = 0.7272
epoch: 100, loss = 0.7242
accuracy: 0.5127
``````

If I use an online sample set of data I get an accuracy of 0.9+ which is really good, the only change that needs to be made to make use the online sample set is to uncomment the following lines of code:

``````#bc = datasets.load_breast_cancer()
#x = bc.data
#y = bc.target
#n_samples, n_features = x.shape
``````

It is really strange that I’m getting such a low accuracy with the local dataset I’m trying use as with the prints statements below the sample online set and the local dataset are near identical except in size so I don’t understand why one can get an accuracy of 0.9+ while the other gets an accuracy of only 0.5ish.

``````print(x)
print(y)
print(x.shape)
print(y.shape)
print(type(x))
print(type(y))
``````

Using The Locally Avaialable Dataset

``````[[ 1.8304332   1.4942976  -2.1410794  ...  1.3968126  -1.5442173
1.6766251 ]
[-1.4084567  -2.5564973   2.6160343  ... -0.58872694  4.597042
-0.1104113 ]
[ 1.7604579   4.608794   -0.14257275 ...  2.1461182   3.0524266
0.9494277 ]
...
[ 0.26332846 -0.31079966  1.830318   ... -2.2811062  -2.1526384
-1.9504634 ]
[-3.0524952  -0.38578448  5.5219817  ... -0.91297805  1.2235076
-0.7969331 ]
[ 0.9841667   0.6342935   0.03264663 ...  1.0182375  -0.11988503
1.1116314 ]]
[0 1 1 ... 1 0 1]
(300000, 12)
(300000,)
<class 'numpy.ndarray'>
<class 'numpy.ndarray'>
``````

Using the online sample dataset (uncommented)

``````[[1.799e+01 1.038e+01 1.228e+02 ... 2.654e-01 4.601e-01 1.189e-01]
[2.057e+01 1.777e+01 1.329e+02 ... 1.860e-01 2.750e-01 8.902e-02]
[1.969e+01 2.125e+01 1.300e+02 ... 2.430e-01 3.613e-01 8.758e-02]
...
[1.660e+01 2.808e+01 1.083e+02 ... 1.418e-01 2.218e-01 7.820e-02]
[2.060e+01 2.933e+01 1.401e+02 ... 2.650e-01 4.087e-01 1.240e-01]
[7.760e+00 2.454e+01 4.792e+01 ... 0.000e+00 2.871e-01 7.039e-02]]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 0 0 1 0 0 1 1 1 1 0 1 0 0 1 1 1 1 0 1 0 0
1 0 1 0 0 1 1 1 0 0 1 0 0 0 1 1 1 0 1 1 0 0 1 1 1 0 0 1 1 1 1 0 1 1 0 1 1
1 1 1 1 1 1 0 0 0 1 0 0 1 1 1 0 0 1 0 1 0 0 1 0 0 1 1 0 1 1 0 1 1 1 1 0 1
1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 1 0 1 1 0 0 1 1 0 0 1 1 1 1 0 1 1 0 0 0 1 0
1 0 1 1 1 0 1 1 0 0 1 0 0 0 0 1 0 0 0 1 0 1 0 1 1 0 1 0 0 0 0 1 1 0 0 1 1
1 0 1 1 1 1 1 0 0 1 1 0 1 1 0 0 1 0 1 1 1 1 0 1 1 1 1 1 0 1 0 0 0 0 0 0 0
0 0 0 0 0 0 0 1 1 1 1 1 1 0 1 0 1 1 0 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1
1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 0 1 1 1 1 0 0 0 1 1
1 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 0
0 1 0 0 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 0 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1
1 0 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 0 1 1 1 1 1 0 1 1
0 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1
1 1 1 1 1 1 0 1 0 1 1 0 1 1 1 1 1 0 0 1 0 1 0 1 1 1 1 1 0 1 1 0 1 0 1 0 0
1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 0 0 0 0 0 0 1]
(569, 30)
(569,)
<class 'numpy.ndarray'>
<class 'numpy.ndarray'>
``````

Sample Set Of Inputs Used From Intended Local Dataset (150/300,000) (Needs to be saved as ‘train-io.txt’ in the same directory as the Python file to be used)

``````-1.99045217 -1.40607965 2.71678582 -3.70629924 -3.14337061e-01 -1.54770974e-01 3.50844296 -1.08004104 -2.43384485e-01 6.89013782e-01 -1.67605624 5.01576543e-02 1
-3.01029538e-01 -4.72911318 6.01167146e-01 1.62611969e-01 3.62557887 -1.45076077 6.01137263e-01 -2.24355449 1.63722894 4.25577822e-02 1.13424037 4.34732584 0
1.09885188 -2.39236136 -3.28692724 1.77307004 1.40922587 -1.39747528e-01 9.35373268e-01 1.54748173 -6.57541866e-01 1.72365529e-01 1.41654990 2.23833608 1
1.17889801 -3.32005518 4.90683895e-01 2.56304614 2.67164645e-01 -3.80436527e-01 -3.02129248 -3.44614518 -2.58423006e-01 -1.73783199 7.27725203 6.92246822e-01 0
8.22938016e-01 3.94808122 -4.21515016 3.33085884e-01 -8.06812221e-01 -1.08405406e-02 -2.51833132 9.71709856e-01 -5.58478894e-01 3.95492213 -8.31784276e-01 4.04523014 1
1.75743713 2.84824302 -1.27702129e-01 -2.50509786 -3.88282246 8.14555801e-01 -1.79830736 1.55416768 2.12638577 4.82215517e-01 -1.18923070 9.02767768e-03 1
-3.06138028 -1.54101035e-01 3.11603730 9.43639689e-01 -1.32776018 3.42432862 1.43936324 -2.12182357 -1.01854679 8.36730188e-01 -5.06089908e-01 -2.07921534 0
2.30487790e-01 7.45982442e-01 -6.78751843e-01 -3.23793317e-01 -3.84737068e-01 9.89033966e-01 -1.49970017 1.63012449 -3.65336996e-01 -2.55591687 1.14968751 -7.48640101e-01 0
-4.82435234e-01 -3.68697214 -2.76750274 -3.84704447e-01 1.96097197 -4.02393873e-01 -1.48450479 -5.34861155e-01 -3.97099775 2.22917894 1.07527652e-01 4.34356890 1
1.09260716 -1.97936942 -3.53048534 9.98976929e-01 3.82695544 1.00563733 1.20487463 6.40798068e-02 3.80468181 3.46850632e-01 -6.66443772e-01 4.10101038 1
-2.77987906e-01 4.30866140e-01 3.40009598 1.50542872 -3.57716511 -2.76879409e-01 -8.25889431e-01 -4.29255353 1.00709224 4.93104869e-01 4.92572898 -2.13072667 1
8.74514415e-01 1.01267371 -2.37627404 -5.38215825e-01 -5.57225987e-01 -4.36341929e-01 -1.81164583 2.11915130 -1.17345865 2.48300038e-02 1.25571411 7.39795873e-01 0
-2.00280376 6.15709367e-01 -1.06682049 -1.73539177 1.15651584 -1.23573041 1.69430038e-01 2.28671814 -3.93934986 1.03060456 -5.69093574 -2.57840979 0
2.60617625e-01 3.15456270 2.92078929e-01 2.81195292 -2.61769193 1.78990718 -1.04412304 -5.73573922e-01 -1.05850956 1.20736263 2.83823697 -1.51473802 1
2.60789031e-01 9.74821202e-01 -2.04432954 9.98022533e-01 -2.22635565 3.07252556 -1.06536161 2.43968291 -1.92167364 1.91222555 -3.27332253 -6.17255207e-01 0
-2.46173532 -2.01571712 1.94405602 -3.14753385e-01 3.54422107 -1.01141623 7.92302803e-01 -1.97893915e-01 1.03894716e-01 -1.93666192 -1.34038508 -1.65052710 1
-1.95861778 -1.47651248 1.84973259 -2.38589385 2.63538478e-01 9.52324226e-01 -3.15503670e-01 -8.59884510e-01 -2.28195158 -7.17481185e-01 -2.22101331e-01 -9.83930977e-01 0
-1.01248647 1.79986720 -7.71369179e-01 -1.12281733e-01 -1.48920994 1.05928022 -1.20796809 -5.28110296e-01 -4.05617508 1.91730940 7.42554578e-01 -1.37507818 1
2.01225466 -1.38009260 6.95320018e-01 1.97844237 3.08713581 -3.62956436 -8.72124080e-01 -3.32340283e-01 1.98508691 -3.65525512 1.56040796 -2.23652758 0
3.00102525 -3.43334802 -6.17977589e-01 1.33968151 6.11488821e-01 4.46359974e-01 -1.62181215 7.69268199e-01 2.92582015 -8.20197601e-01 1.16980342 3.61828347 1
-1.24974354 -1.60724288 2.16492041 -2.39537128 3.56895868 -3.61436089 1.99851472 -2.84486815 3.64098411 -1.38603068e-01 9.49372521e-02 2.00912416 0
6.60007069e-01 -2.34196630 8.43437251e-01 1.06902166 2.15084932e-02 -1.62142382 5.94795228e-01 -1.85403338 1.32944650 -7.85104052e-01 5.98350396 1.11672724 1
-8.13673247e-01 -3.35189434 2.30034603 2.55960419 7.19306203e-01 -7.16594033e-01 6.74980779e-01 -1.56094406 -3.94020523e-01 -2.19806019e-01 2.91600364 -1.38456581e-01 1
2.88625846e-01 1.95024766 -2.80730507 -5.04139795e-01 -1.30620202 3.34701828e-01 1.21924626 6.54270953e-01 -1.31668027 4.15068718 1.11628232 4.60273037 1
1.42240617 -1.52786409 1.94582838 4.15441726e-01 -2.45614436e-01 -2.49177143 -2.24779719 -2.10736413 4.62016747e-01 3.77047389e-01 2.70047198 1.10223965e-01 1
4.97250924e-02 1.84492469 -2.09121308e-01 2.79241059e-01 -1.27565380 8.76631371e-01 1.38104035e-01 1.93882566 1.25010393 1.77516266e-01 -1.18198474 4.28310892e-01 1
2.43787551e-01 -8.77814582e-01 8.44480348e-01 4.53061837 -9.79232303e-01 1.16220415 -1.01839850 -1.54194710 7.89990530e-02 -3.27166244 4.42766359 -4.82145758 1
-5.90232641e-01 -2.68044578 3.73950509e-01 1.65193898 2.47471044 -6.56377707e-01 1.63348829 -1.70227839 -3.74047732e-01 -4.76014487e-01 4.01968901 1.53975718e-01 1
-4.60462291e-01 -6.22415284e-01 1.19632802 2.34732443 -1.66215026 1.10052807 -6.27810799e-01 -2.06104099 -7.32079579e-01 -3.19731536e-02 1.99308086 -2.71129226 0
2.93026821e-01 -1.27577085 2.44644987 -3.54946646 -4.87462606e-02 -1.37029122 1.89517775e-01 2.51677138 1.05652047 -3.65718360 -3.12442272 -3.67560924 1
-1.28143493 -1.03075991 8.18856150e-02 -1.09452237 1.62149019e-01 -1.09857845e-01 3.11924251 -4.50234039e-01 -1.75974663 -1.67415043e-01 1.32144580e-01 -5.72724237e-01 0
-2.63031927e-01 7.61118748e-01 -6.87239876e-01 1.14774710 -2.08398106 2.26786864 4.08815365e-01 -1.32038313 2.23316330e-01 2.86616349 4.17287920 3.63608562 0
6.63113250e-01 -3.96018623 -5.73021296e-01 2.29293385 4.01986173e-02 2.43747203 -2.89111164 -1.24720297 -1.46618565 -3.36573126e-02 1.37912438 2.05394006 1
1.06798486e-01 -2.77414142 1.96625812 -4.30559691 1.86741799 1.18880358e-01 1.31952116 3.04984523 1.56535606 -3.39020590 -4.70678287 7.92359002e-02 0
1.87383472 -3.91166543 -1.09488410 -7.26581039e-01 -9.13651777e-03 -1.08976009 -1.42488400 3.26690407e-01 2.47642054e-01 -2.23164831 2.76553986 -4.60747305e-01 1
3.33523273e-01 -4.12810109e-01 -3.40122132 -3.94550550e-01 2.93208458 -1.87029798 2.72906647e-01 1.13199527 5.91035326e-01 2.24309786 -5.59897087e-01 5.66058945 1
1.72231444 3.71991324 -3.91546557 -2.26934462e-01 -8.29375218e-01 -9.42668171e-01 -1.61990027 8.13791255e-01 -2.31212947 2.94626031 -5.37643814e-01 5.15769167e-01 0
-1.64656362 -1.10954738 2.39813734 1.96249452 1.66441076e-01 8.66178206e-01 2.05049544 -1.23216013 -1.43094971 -7.44178763e-01 9.48777857e-01 -2.02534343 1
1.73855348 -5.53782190 -6.48851617e-01 -7.91664531e-01 2.13814094 -1.80545414e-01 1.78194252e-01 -6.43020613e-01 4.31186541 -3.66270956 2.82157804 2.71136265 1
-8.87566251e-01 -1.77773574 1.34194811 6.81862863e-01 2.23841597e-01 3.04617433e-01 5.77932560e-01 1.06971578 -7.69409560e-01 -2.91748424e-01 -2.70308382 -1.95605509 0
-4.20572091e-01 3.99217311 -7.29230953e-01 1.47574009 -1.22779197 -5.79486683e-01 1.28798133 -1.35672449 -5.91131009e-01 1.84120357 1.11045316 -2.58050900 0
-1.62840094e-02 1.74771115 -7.52295707e-01 2.62941500 -9.16626077e-01 -1.21722898 1.78116381 -1.58876586 -1.19170240 7.14179141e-02 3.66111231 -3.09918691 0
-1.81674620 1.12419300 1.80180619 1.21744150 -2.26994272 2.24806159 1.00406612 9.53202960e-02 -1.50774030 -1.13149569 2.28003777 -2.56460453 0
3.41775319e-01 3.13306537e-01 -1.85592820 -3.03912856 -4.62861452e-01 -1.68978602 -2.51214948 8.18191403e-01 -1.94236187 4.91682185e-01 -1.70550469 9.96096189e-01 0
9.97665809e-01 -2.55123055 -2.36339827 -2.43837174 3.67386376 -6.64247180e-01 1.96878439 1.41325975 2.10175154 -2.15540633e-01 -3.46505371 3.08260778 1
7.41206075e-01 5.25938253e-02 -1.63407505 -2.94868685 1.68701321 -3.18876113e-01 1.66284488 2.27586665 3.31022383 -1.89022837 -2.11058711 2.93154892e-01 1
-1.45125874 -2.87047212e-01 -3.95522232e-01 2.82596238 -6.62968333e-01 7.74603777e-01 -2.41366456 -8.40496347e-01 -4.24148741 2.30185734 1.87320437e-01 9.21871648e-01 1
4.80180980e-01 -3.22391839 3.02077702 2.11114219e-01 1.22601595e-03 2.07767955e-01 1.24429967 -4.35541181 2.60551345 8.89125744e-01 4.98055440 2.54253354 1
-1.00008123e-01 -1.58087204 4.69136798e-01 1.30944137 8.36538385e-01 -9.23272536e-02 2.59016974e-01 2.51590232e-01 -1.59529284 -2.10540651e-01 -1.54982679 -1.59202729 1
1.33005590 -8.90834865e-01 -6.07503224e-01 -6.88872087e-01 -2.86428109 5.56980638e-01 -4.07025088 2.44317241 -2.41680442 -3.27593333e-01 -1.47266564 -1.01572028 0
2.47064813 -1.98032429 1.94198681 -1.30914558 1.73696288 -1.70342589 1.61138266e-01 -6.82397038e-01 5.71809726 -4.05905752 2.69289294 5.90212774e-01 1
3.13886569e-01 -1.86690128 1.54766352 -6.48078985e-01 1.36924175 -5.55395241e-01 -1.74338288 1.51646874 7.32823137e-01 -1.81922576 -2.51211499 7.78449209e-01 1
-9.17662863e-01 -6.92375777e-01 1.63559783 4.19548782e-01 9.19659494e-01 -9.67884041e-02 6.85198449e-01 -3.67690610 1.88990301 7.51537502e-01 2.36336409 3.63748264e-01 0
-9.80011156e-01 3.38744850e-01 9.21666550e-01 2.10984707 -1.51896097 2.51128786 -1.30601822 -3.25038508e-02 3.50474264e-01 -1.28485647 9.77570988e-01 -1.20937358 1
1.34161949 -1.22712754 1.39019532e-01 -2.11440927e-01 -1.15163276 1.69007507 -1.82377540 1.21873133 3.68924383 -1.32552347 5.66901989e-01 2.16897704 0
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``````