I would like to know if the result is quite reasonable for logistics regression model to predict churn for markteting
Confusion Matrix
[[ 14 31]
[ 8 146]]
** precision recall f1-score support**
0 0.64 0.31 0.42 45
1 0.82 0.95 0.88 154
accuracy 0.80 199
macro avg 0.73 0.63 0.65 199
weighted avg 0.78 0.80 0.78 199
0 = not churn
1 = churn
The reason I ask for this question is I'm not sure that though the accuracy is not that bad, the performance of model could deem good
if you look at the confusion matrix, the point such as 31(FP) seems relatively big
Thank you for sharing your understanding in advance.
I check this blog posting to undertsand but I'm not sure I can still see the accuracy is good
https://towardsdatascience.com/understanding-confusion-matrix-a9ad42dcfd62
Related
I'm implementing a 7-class classification task with normalised features and one-hot encoded labels. However, the training and validation accuracies have been extremely poor.
As shown, I normalised features from with StandardScaler() method and each feature vector turns out a 54-dim numpy array. Also, I one-encoded labels in the following manner.
As illustrated below, the labels are (num_y, 7) numpy arrays.
My network architecture:
It is shown here how I designed my model. And I'm wonder if the poor result has something to do with the selection of loss function (I've been using Categorical Cross-Entropy)
I appreciate any response from you. Thanks a lot!
The use of accuracy is obviously wrong. The code I refer to is not provided in your question, but I can speculate that you are comparing the true labels with your model outputs. Your model probably returns a vector of dimensionality 7 which constitutes a probability density function over the classes (due to the softmax activation in your final layer) like this:
model returns: (0.7 0 0.02 0.02 0.02 0.04 0.2) -- they sum to 1 because they represent probabilities
and then you are comparing these numbers with: (1 0 0 0 0 0 0)
what you have to do is translate the model output to the corresponding predicted label ((0.7 0 0.02 0.02 0.02 0.04 0.2) corresponds to (1 0 0 0 0 0 0) because the first output neuron has the larger value (0.7)). You may do that by applying a max function after your model outputs.
To make sure thats whats wrong with your problem formulation print the vector you are comparing with the true labels to get your accuracy and check if they are 7 numbers that sum up to 1.
I am trying to find optimal parameters of my neural network model implemented on octave, this model is used for binary classification and 122 features (inputs) and 25 hidden units (1 hidden layer). For this I have 4 matrices/ Vectors:
size(X_Train): 125973 x 122
size(Y_Train): 125973 x 1
size(X_Test): 22543 x 122
size(Y_test): 22543 x 1
I have used 20% of the training set to generate a validation set (XVal and YVal)
size(X): 100778 x 122
size(Y): 100778 x 1
size(XVal): 25195 x 122
size(YVal): 25195 x 1
size(X_Test): 22543 x 122
size(Y_test): 22543 x 1
The goal is to generate the Learning curves of the NN. I have learned (the hard way xD) that this is very time consuming because I used the full size of Xval and X for this.
I don't know if there is an alternative solution for this. I am thinking to reduce the size of the training vector X (like 5000 samples for example), but I don't know if I can do that, or if the results will be biased since I'll only use a portion of the training set?
Bests,
The total number of parameters above is around 3k (122*25 + 25*1), which is not huge for one example. Since the number of examples is large, you might want to use stochastic gradient descent or mini-batches instead of gradient descent.
Note that Matlab and Octave are slow in general, specially with loops.
You need to write the code which uses matrix operations rather than loops for the speed to be manageable in Matlab/Octave.
I am working on a GUI to plot ac712 current data to MATLAB GUI. The problem is I cannot plot the data properly. The plot seems to be triangular and not sinusoidal curve. Also the current values are correct but I think x-axis values are incorrect. Please help.
clear all
clc
a = arduino('com3','uno');
samples = 200
for i = 1:201
x = [0:0.001:2];
y = zeros(1,201);
b = a.readVoltage(0);
y(i) = ((b-2.5)/.234);
i = i+1
pause (0.006)
end
figure(1)
plot(x,y)
Fig. Plot obtained using MATLAB
When I use arduino only for the same, the values for current are as follows:
0.46
-0.69
1.04
-0.94
0.81
-0.29
-0.06
0.71
-0.83
1.08
-0.81
0.62
0.04
-0.31
0.87
-0.87
1.1
-0.67
0.37
0.27
-0.56
1.02
-0.92
0.94
-0.46
0.08
0.52
-0.71
1.04
which when plotted using Excel is as follows:
Fig. Plot obtained with Arduino current data and x axis values as: 1-29
Are the time scales of both graphs the same?
What is the frequency of the signal you are trying to read?
Because the ACS712 uses chopper stabilization, the output signal is similar to your plot.
You may need to add or change the capacitor between the filter input pin and ground as shown on the typical application figure on the front page of the ACS712 datasheet.
I have a set of data that I need to find a best fit polynomial for. Currently I am using python's polyfit, but there are obvious outliers (when the value drops to 0 on the parabola) that affect my polynomial. How would I go about filtering out the unwanted drops in the data so that I get a best fit parabola that does not include the noise?
There is an obvious parabolic path that I would like to get the polynomial to.
Is there a matlab function that I could use to filter out the noise so polyfit would be a better representation of the parabolic data path?
You can filter a 1D function by convolution.
I used this data set:
y=[0 10 50 80 100 0 100 0 110 0 130 130 0 130 0 110 0 100 0 70 0 50 20 10 0];
And when convoluting with
ones(1,10)/10
like this:
convolved=conv(y,ones(1,10)/10);
and then normalizing it to the same maximal value,
convolved2=convolved*max(y)/max(convolved);
this is the result:
And when convoluting with
convolved=conv(y,ones(1,50)/50);
This is the result (again multiplied by the max of y and divided by the max of convolved):
You just need to adjust the filter the way you need. Hopes that this helps.
`2.4 2.34 2.21 1.90 1.4 0.83 0
2.34 2.42 2.16 1.79 1.3 0.7 0
2.21 2.16 1.99 1.64 1.16 0.51 0
1.90 1.79 1.64 1.30 0.75 0 0
1.4 1.34 1.16 0.75 0 0 0
0.83 0.75 0.51 0 0 0 0
0 0 0 0 0 0 0
`
This is what my matrix looks like at one point in time and Id like to calculate the average slope or gradient of the surface( An indicator of steepness).
It seems like MatLab should have a built in function for this sort of thing but I can find one. I have also tried a code that I wrote but it is not accurate.
Best,
Abid
the gradient is vectorial, so you can treat the two dimensions separately.
Calculating the gradient consists of calculating the first derivative to every parameter of the function. In the discrete domain, this can be done by taking the finite differences: http://en.wikipedia.org/wiki/Finite_difference
If you want it exact and calculate the gradient at each point, you can't just take the forward or backward differences, because at the edge, you don't always have a forward (or backward) point.
But I think if you just want the mean of the gradient, you don't have to bother with the edges and you can just compute the finite differences for the central points between your original points by using diff:
dx = diff(data,1,1);
dy = diff(data,1,2);
mean_gradient = [mean(dx(:)) mean(dy(:))]
assuming equal spacing of the parameters of cours, else you'll have to divide dx and dy by the step sizes.
For the data you provided, this results in:
mean_gradient =
-0.26381 -0.26381
which matches the plot you've made that shows that the function is monotonically decreasing and symmetric in the parameters.
One way to do this is to perform multiple linear regression, for example by using regress.
A two-dimensional regression is a best-fit plane, from which you can calculate a slope value. The link has a nice example with code and a plot of the results.