Questions tagged [machine-learning]

Machine Learning is a subfield of computer science that draws on elements from algorithmic analysis, computational statistics, mathematics, optimization, etc. It is mainly concerned with the use of data to construct models that have high predictive/forecasting ability. Topics include modeling building, applications, theory, etc.

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15
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3answers
4k views

Can a neural network compute $y = x^2$?

In spirit of the famous Tensorflow Fizz Buzz joke and XOr problem I started to think, if it's possible to design a neural network that implements $y = x^2$ function? Given some representation of a ...
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4answers
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What initial steps should I use to make sense of large data sets, and what tools should I use?

Caveat: I am a complete beginner when it comes to machine learning, but eager to learn. I have a large dataset and I'm trying to find pattern in it. There may / may not be correlation across the data,...
14
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2answers
26k views

How to calculate VC-dimension?

Im studying machine learning, and I would like to know how to calculate VC-dimension. For example: $h(x)=\begin{cases} 1 &\mbox{if } a\leq x \leq b \\ 0 & \mbox{else } \end{cases} $, with ...
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2answers
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Validation vs. test vs. training accuracy. Which one should I compare for claiming overfit?

I have read on the several answers here and on the Internet that cross-validation helps to indicate that if the model will generalize well or not and about overfitting. But I am confused that which ...
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2answers
8k views

Difference between LSTM cell state and hidden state

LSTM cells consist of two types of states, the cell state and hidden state. How do cell and hidden states differ, in terms of their functionality? What information do they carry?
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1answer
669 views

data science / machine learning resources? [closed]

In a few weeks I'm starting a new job that will be involved in machine learning and data science. I have a masters degree in probability / mathematics but I have no knowledge of machine learning and ...
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2answers
8k views

local minima vs saddle points in deep learning

I heard Andrew Ng (in a video I unfortunately can't find anymore) talk about how the understanding of local minima in deep learning problems has changed in the sense that they are now regarded as less ...
12
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2answers
14k views

Feature Scaling both training and test data

It is stated that for: Feature Normalization - The test set must use identical scaling to the training set. And the point is given that: Do not scale the training and test sets using different ...
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1answer
38k views

How to download dynamic files created during work on Google Colab?

I have two different files and on the first, I tried to save data to file as: np.save(open(Q1_TRAINING_DATA_FILE, 'wb'), q1_data) On second file, i'm trying to ...
21
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2answers
17k views

Doc2Vec - How to label the paragraphs (gensim)

I am wondering how to label (tag) sentences / paragraphs / documents with doc2vec in gensim - from a practical standpoint. Do you need to have each sentence / paragraph / document with its own ...
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2answers
8k views

How to train model to predict events 30 minutes prior, from multi-dimensionnal timeseries

Experts in my field are capable of predicting the likelyhood an event (binary spike in yellow) 30 minutes before it occurs. Frequency here is 1 sec, this view represents a few hours worth of data, i ...
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2answers
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Solving a system of equations with sparse data

I am attempting to solve a set of equations which has 40 independent variables (x1, ..., x40) and one dependent variable (y). The total number of equations (number of rows) is ~300, and I want to ...
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2answers
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Machine Learning Steps

Which of the below set of steps options is the correct one when creating a predictive model? Option 1: First eliminate the most obviously bad predictors, and preprocess the remaining if needed, then ...
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4answers
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Train/Test Split after performing SMOTE

I am dealing with a highly unbalanced dataset so I used SMOTE to resample it. After SMOTE resampling, I split the resampled dataset into training/test sets using the training set to build a model and ...
11
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2answers
8k views

Why large weights are prohibited in neural networks?

Why weights with large values cause neural networks to be overfitted, and consequently we use approaches like regularization to neutralize weights with large values?
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3answers
2k views

Statistics + Computer Science = Data Science? [closed]

i want to become a data scientist. I studied applied statistics (actuarial science), so i have a great statistical background (regression, stochastic process, time series, just for mention a few). But ...
10
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4answers
9k views

Is PCA considered a machine learning algorithm

I've understood that principal component analysis is a dimensionality reduction technique i.e. given 10 input features, it will produce a smaller number of independent features that are orthogonal and ...
9
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5answers
13k views

In which epoch should i stop the training to avoid overfitting

I'm working on an age estimation project trying to classify a given face in a predefined age range. For that purpose I'm training a deep NN using the keras library. The accuracy for the training and ...
8
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2answers
11k views

How to plot cost versus number of iterations in scikit learn?

One of the recommendations in the Coursera Machine Learning course when working with gradient descent based algorithms is: Debugging gradient descent. Make a plot with number of iterations on the x-...
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1answer
4k views

How to interpret Variance Inflation Factor (VIF) results?

From various books and blog posts, I understood that the Variance Inflation Factor (VIF) is used to calculate collinearity. They say that VIF till 10 is good. But I have a question. As we can see in ...
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4answers
7k views

Exceptionally high accuracy with Random Forest, is it possible?

I need your help to find a flaw in my model, since it's accuracy (95%) is not realistic. I'm working on a classification problem using Randomforest, with around 2500 positive case and 15000 negative ...
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1answer
93 views

Class imbalance strategies

When dealing with the class imbalance problem in a binary classifier, there are three ways I know of to address it: over-sampling, under-sampling and using cost-sensitive methods. Are there any ...
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1answer
3k views

When does decision tree perform better than the neural network?

I was experimenting with different modelling methods including KNN, Decision Trees, Neural Networks and SVN and trying to fit my data to see which works the best. To my surprise, the decision tree ...
6
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1answer
146 views

Mapping of categorical features into binary indicator features

I am currently reading an introductory machine learning book by Daumé (ch. 03, p. 30) and when discussing the mapping of categorical features with "n" possible values into "n" binary indicator ...
6
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1answer
2k views

Correlation between specific columns of a data set

I have a CSV file which has 150 columns belonging to 7 categories but I want a correlation between 2 categories. The categories are movies and music, 12 and 19 columns respectively. Is there a way ...
3
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2answers
211 views

Time-series multi-step generalization from single step model

I have built a generic stacked lstm model of the form: ...
3
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1answer
453 views

Why does putting a 1/2 in front of the squared error make the math easier?

Per wiki, the mean squared error (MSE) looks like: $$ \operatorname {MSE} ={\frac {1}{m}}\sum _{i=1}^{m}(y_{i}-{\hat y_{i}})^{2} $$ The professor added a $1\over2$ in front of the formula and ...
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1answer
1k views

Cluster documents and identify the prominent document in the cluster?

I have a set of documents as given in the example below. ...
5
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1answer
3k views

What is the difference between C and lambda in the context of an SVM?

I don't understand the difference between the parameter $C$ and $\lambda$ in terms of the SVM. It seems to me that they are both involved in regulating over-fitting of the data. What difference ...
3
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4answers
560 views

What is the difference between classification and regression?

I understand classification....a discrete response or category, like animal is dog or cat. The author says..."Regression techniques predict continuous changes such as the change in temperature, power ...
2
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2answers
430 views

Normalizing the final weights vector in the upper bound on the Perceptron's convergence

The convergence of the "simple" perceptron says that: $$k\leqslant \left ( \frac{R\left \| \bar{\theta} \right \|}{\gamma } \right )^{2}$$ where $k$ is the number of iterations (in which the weights ...
2
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2answers
2k views

Explain Binary Classification with output 0.5 (True)

What is the interpretation of output 0.5 of a typical classifier? I made a prediction and the probability of that data point being from the True class is 0.5.
2
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4answers
1k views

Best methods to solve class imbalance problem and why?

I have a data set where I need to detect fraud. 99% are not fraud and 1% are. What methods can be used to solve problems where classes are imbalanced?
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2answers
111 views

What does $\mathbf{w^Tx}+w_0$ graphically mean in the discriminant function?

I found a post explaining the discriminant function very detailed. But I am still confused about the function $g(\mathbf{x})=\mathbf{w^Tx}+w_0$ in 9.2 Linear Discriminant Functions and Decision ...
168
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6answers
262k views

How to draw Deep learning network architecture diagrams?

I have built my model. Now I want to draw the network architecture diagram for my research paper. Example is shown below:
104
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5answers
63k views

Why do cost functions use the square error?

I'm just getting started with some machine learning, and until now I have been dealing with linear regression over one variable. I have learnt that there is a hypothesis, which is: $h_\theta(x)=\...
92
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4answers
91k views

Advantages of AUC vs standard accuracy

I was starting to look into area under curve(AUC) and am a little confused about its usefulness. When first explained to me, AUC seemed to be a great measure of performance but in my research I've ...
99
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9answers
125k views

When should I use Gini Impurity as opposed to Information Gain (Entropy)?

Can someone practically explain the rationale behind Gini impurity vs Information gain (based on Entropy)? Which metric is better to use in different scenarios while using decision trees?
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6answers
124k views

strings as features in decision tree/random forest

I am doing some problems on an application of decision tree/random forest. I am trying to fit a problem which has numbers as well as strings (such as country name) as features. Now the library, scikit-...
66
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5answers
96k views

GBM vs XGBOOST? Key differences?

I am trying to understand the key differences between GBM and XGBOOST. I tried to google it, but could not find any good answers explaining the differences between the two algorithms and why xgboost ...
41
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6answers
45k views

What is the difference between model hyperparameters and model parameters?

I have noticed that such terms as model hyperparameter and model parameter have been used interchangeably on the web without prior clarification. I think this is incorrect and needs explanation. ...
18
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9answers
3k views

How Do I Learn Neural Networks?

I'm a freshman undergraduate student (mentioning this so you may forgive my unfamiliarity) who is currently doing research using neural networks. I've coded a three-node neural network (that works) ...
32
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1answer
33k views

RNN's with multiple features

I have a bit of self taught knowledge working with Machine Learning algorithms (the basic Random Forest and Linear Regression type stuff). I decided to branch out and begin learning RNN's with Keras. ...
43
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2answers
63k views

Merging two different models in Keras

I am trying to merge two Keras models into a single model and I am unable to accomplish this. For example in the attached Figure, I would like to fetch the middle layer $A2$ of dimension 8, and use ...
53
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8answers
15k views

Why Is Overfitting Bad in Machine Learning?

Logic often states that by overfitting a model, its capacity to generalize is limited, though this might only mean that overfitting stops a model from improving after a certain complexity. Does ...
26
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3answers
2k views

Why are NLP and Machine Learning communities interested in deep learning?

I hope you can help me, as I have some questions on this topic. I'm new in the field of deep learning, and while I did some tutorials, I can't relate or distinguish concepts from one another.
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4answers
50k views

Do Random Forest overfit?

I have been reading around about Random Forests but I cannot really find a definitive answer about the problem of overfitting. According to the original paper of Breiman, they should not overfit when ...
39
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3answers
25k views

Difference between OrdinalEncoder and LabelEncoder

I was going through the official documentation of scikit-learn learn after going through a book on ML and came across the following thing: In the Documentation it is given about ...
24
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5answers
71k views

Validation loss is not decreasing

I am trying to train a LSTM model. Is this model suffering from overfitting? Here is train and validation loss graph:
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3answers
14k views

Cross validation Vs. Train Validate Test

I have a doubt regarding the cross validation approach and train-validation-test approach. I was told that I can split a dataset into 3 parts: Train: we train the model. Validation: we validate and ...

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