How to build a binary classifier/predictor for 1-d vector data in Python
[Disclaimer] This is my first excursion into machine learning.
I have a list of 1-d numpy real vectors that represent experimental conditions known to be associated to two mutually exclusive classes. To each vector a 1 or 0 can be assigned as the class label.
What is the best way to construct a classifier/predictor using these classes in Python such that the differences between the two classes are maximized?
machine-learning classification svm prediction
asked Nov 20 at 23:08
Santiago Nuñez-Corrales
406
add a comment |
[Disclaimer] This is my first excursion into machine learning.
I have a list of 1-d numpy real vectors that represent experimental conditions known to be associated to two mutually exclusive classes. To each vector a 1 or 0 can be assigned as the class label.
What is the best way to construct a classifier/predictor using these classes in Python such that the differences between the two classes are maximized?
machine-learning classification svm prediction
asked Nov 20 at 23:08
Santiago Nuñez-Corrales
406
add a comment |
[Disclaimer] This is my first excursion into machine learning.
I have a list of 1-d numpy real vectors that represent experimental conditions known to be associated to two mutually exclusive classes. To each vector a 1 or 0 can be assigned as the class label.
What is the best way to construct a classifier/predictor using these classes in Python such that the differences between the two classes are maximized?
machine-learning classification svm prediction
asked Nov 20 at 23:08
Santiago Nuñez-Corrales
406
[Disclaimer] This is my first excursion into machine learning.
I have a list of 1-d numpy real vectors that represent experimental conditions known to be associated to two mutually exclusive classes. To each vector a 1 or 0 can be assigned as the class label.
What is the best way to construct a classifier/predictor using these classes in Python such that the differences between the two classes are maximized?
machine-learning classification svm prediction
machine-learning classification svm prediction
asked Nov 20 at 23:08
Santiago Nuñez-Corrales
406
asked Nov 20 at 23:08
Santiago Nuñez-Corrales
406
asked Nov 20 at 23:08
Santiago Nuñez-Corrales
406
asked Nov 20 at 23:08
Santiago Nuñez-Corrales
406
asked Nov 20 at 23:08
Santiago Nuñez-Corrales
406
406
add a comment |
add a comment |
1 Answer
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Let's say you have 1000 vectors with 10 values. Your x data has shape (1000,10), y data (1000,1) (it's either 0 or 1, according to class). You want to predict y from x.
The simplest model could look like (using Keras):
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
mdl = Sequential() // create model
mdl.add(Dense(8, input_shape=(10,), activation='sigmoid'))
mdl.add(Dense(1, activation='sigmoid')
mdl.compile(optimizer = 'adam', loss='binary_crossentropy')
mdl.fit(x, y, epochs = 30)
Note that I can use sigmoid in the last layer of classification problem only if there are 2 classes. With more classes you should use softmax.
I recommend you check this page: https://keras.io/
Also, I think keras is better to begin with than tensorflow.
answered Nov 20 at 23:25
Róbert Druska
1794
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Let's say you have 1000 vectors with 10 values. Your x data has shape (1000,10), y data (1000,1) (it's either 0 or 1, according to class). You want to predict y from x.
The simplest model could look like (using Keras):
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
mdl = Sequential() // create model
mdl.add(Dense(8, input_shape=(10,), activation='sigmoid'))
mdl.add(Dense(1, activation='sigmoid')
mdl.compile(optimizer = 'adam', loss='binary_crossentropy')
mdl.fit(x, y, epochs = 30)
Note that I can use sigmoid in the last layer of classification problem only if there are 2 classes. With more classes you should use softmax.
I recommend you check this page: https://keras.io/
Also, I think keras is better to begin with than tensorflow.
answered Nov 20 at 23:25
Róbert Druska
1794
add a comment |
Let's say you have 1000 vectors with 10 values. Your x data has shape (1000,10), y data (1000,1) (it's either 0 or 1, according to class). You want to predict y from x.
The simplest model could look like (using Keras):
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
mdl = Sequential() // create model
mdl.add(Dense(8, input_shape=(10,), activation='sigmoid'))
mdl.add(Dense(1, activation='sigmoid')
mdl.compile(optimizer = 'adam', loss='binary_crossentropy')
mdl.fit(x, y, epochs = 30)
Note that I can use sigmoid in the last layer of classification problem only if there are 2 classes. With more classes you should use softmax.
I recommend you check this page: https://keras.io/
Also, I think keras is better to begin with than tensorflow.
answered Nov 20 at 23:25
Róbert Druska
1794
add a comment |
Let's say you have 1000 vectors with 10 values. Your x data has shape (1000,10), y data (1000,1) (it's either 0 or 1, according to class). You want to predict y from x.
The simplest model could look like (using Keras):
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
mdl = Sequential() // create model
mdl.add(Dense(8, input_shape=(10,), activation='sigmoid'))
mdl.add(Dense(1, activation='sigmoid')
mdl.compile(optimizer = 'adam', loss='binary_crossentropy')
mdl.fit(x, y, epochs = 30)
Note that I can use sigmoid in the last layer of classification problem only if there are 2 classes. With more classes you should use softmax.
I recommend you check this page: https://keras.io/
Also, I think keras is better to begin with than tensorflow.
answered Nov 20 at 23:25
Róbert Druska
1794
Let's say you have 1000 vectors with 10 values. Your x data has shape (1000,10), y data (1000,1) (it's either 0 or 1, according to class). You want to predict y from x.
The simplest model could look like (using Keras):
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
mdl = Sequential() // create model
mdl.add(Dense(8, input_shape=(10,), activation='sigmoid'))
mdl.add(Dense(1, activation='sigmoid')
mdl.compile(optimizer = 'adam', loss='binary_crossentropy')
mdl.fit(x, y, epochs = 30)
Note that I can use sigmoid in the last layer of classification problem only if there are 2 classes. With more classes you should use softmax.
I recommend you check this page: https://keras.io/
Also, I think keras is better to begin with than tensorflow.
answered Nov 20 at 23:25
Róbert Druska
1794
edited Nov 20 at 23:31
answered Nov 20 at 23:25
Róbert Druska
1794
answered Nov 20 at 23:25
Róbert Druska
1794
answered Nov 20 at 23:25
Róbert Druska
1794
1794
add a comment |
add a comment |
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