Linear Regression problem in predicting values - machine-learning

I am trying to predict values using linear regression . when I use Here
it pops like this error
How to reshape this?

The prediction function takes a 2-D array as an input, so you can use reg.predict([[60]]) instead


Why we have to reshape the array values when we are going to predict results using the predict() method of linear regression

While we are predicting it is mandatory to give the input to predict() as the 2-D array. why it is necessary ?
predict requires the argument as 2-D array because it transforms the rows and columns in the features and observation vecotr

Predicting inputs in neural network

Is it possible to predict inputs in "Keras neural network" for a particular output?
For example, I have a dataset with 28 inputs and 3 outputs. So, I have trained the model in Keras which works fine. Now, I have to enter the particular values in outputs and I have to predict that what will be the inputs for that particular output.
I'm not 100% sure I understand the question correctly, but if you're trying to build a model that can take inputs and predict outputs, then you will need to train a second model to predict inputs from outputs, where you swap the inputs and outputs so that outputs are your inputs, and your inputs are the outputs. Although this might be annoying, you might have to build a separate network to predict each of your input variables.
To get around this problem, you can consider autoencoders if you're okay with getting a close approximation of the input. An autoencoder is an unsupervised artificial neural network that learns how to efficiently compress and encode data then learns how to reconstruct the data back from the reduced encoded representation to a representation that is as close to the original input as possible (you can read more here:
Yes it is definitely possible to predict inputs from the output. In fact, what you're describing is essentially an autoencoder.
Let's say you have a NN trained on MNIST. If you then use the outputs of the classification layer to train the decoder of an auto encoder, you will get a rough indication of the input.
However this is not the best way to do it. The best way to do it is to simply have the latent space be considered the "output", then feed this output into:
a): A 1 layer classification to give you the predicted output and
b): the decoder
This will give you the predicted output and the original image

What is the difference between Keras model.evaluate() and model.predict()?

I used Keras biomedical image segmentation to segment brain neurons. I used model.evaluate() it gave me Dice coefficient: 0.916. However, when I used model.predict(), then loop through the predicted images by calculating the Dice coefficient, the Dice coefficient is 0.82. Why are these two values different?
The model.evaluate function predicts the output for the given input and then computes the metrics function specified in the model.compile and based on y_true and y_pred and returns the computed metric value as the output.
The model.predict just returns back the y_pred
So if you use model.predict and then compute the metrics yourself, the computed metric value should turn out to be the same as model.evaluate
For example, one would use model.predict instead of model.evaluate in evaluating an RNN/ LSTM based models where the output needs to be fed as input in next time step
The problem lies in the fact that every metric in Keras is evaluated in a following manner:
For each batch a metric value is evaluated.
A current value of loss (after k batches is equal to a mean value of your metric across computed k batches).
The final result is obtained as a mean of all losses computed for all batches.
Most of the most popular metrics (like mse, categorical_crossentropy, mae) etc. - as a mean of loss value of each example - have a property that such evaluation ends up with a proper result. But in case of Dice Coefficient - a mean of its value across all of the batches is not equal to actual value computed on a whole dataset and as model.evaluate() uses such way of computations - this is the direct cause of your problem.
The keras.evaluate() function will give you the loss value for every batch. The keras.predict() function will give you the actual predictions for all samples in a batch, for all batches. So even if you use the same data, the differences will be there because the value of a loss function will be almost always different than the predicted values. These are two different things.
It is about regularization. model.predict() returns the final output of the model, i.e. answer. While model.evaluate() returns the loss. The loss is used to train the model (via backpropagation) and it is not the answer.
This video of ML Tokyo should help to understand the difference between model.evaluate() and model.predict().

Classifier for data with varying dimensionality

I need to train a classifier with data whose dimensionality can vary. For example (and this is made-up date for illustration):
and so on...
I am trying to train a Linear SVM using scikit-learn which requires the dimensionality to be fixed. A simple zero-padding of the smaller dims to match the dim of the largest, is giving me disappointing results.
Should I be using a different classifier for such data? How should I approach this?
Feature hashing is the algorithm you need to use to convert your variable-length input into constant-length input. Then, you could use your transformed vectors with any appropiate learning algorithm.
Wikipedia: Feature Hashing
Try padding with feature mean/median, that's another way to deal with missing data.
Are those measurements made in the same points/features ?

Begining to code Logistic regression in java

I want to code the logistic regression(classification problem) algorithm using java -
Hypothesis is -
Can anyone please tell me what −(−θ to the power T) is?
I was able to code linear regression its hypothesis is which is relatively easy but can not start off with logistic regression.
ΘT is the transpose of parameters vector Θ and ΘTx is the linear combination of input features.If you know linear regression then you can think ΘTx as a output of linear regression. Look at the figure below.
The first part is the linear regression. The output of the linear regression is
. Since logistic regression is not a regression but a classification problem, your output shouldn't be continuous. Instead you require a binary output for any inputs. For this you need a function that maps the range of input to the value between 0 and 1 so that you can apply some threshold to the output to get the classification. And the suitable function for this would be sigmoid function as you mentioned.
Regrading your question, the output of linear regression can be written as
The term = ΘTx is the vectorized implementation of output of linear regression. So ΘT is nothing but a transpose of parameter vector. This can be understood by following mathematical operations.
For details in logistic regression and coding check this link.
The ΘT represenets transponse of theta matrix. Where theta matrix is matrix of features. When writing code for those algorthms, I strongly advice yout to use first MATLAB or OCTAVE software first for calculating matrices. Then, when you are sure that your algorithm is working correctly implement it in JAVA.