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H: Summing three lexicon based approach methods for sentiment analysis?
I'm doing sentiment analysis using a lexicon based approach and I have a bunch of news headlines that needs to be categorized as negative, positive and neutral or within a scale ranging from -1 (very negative sentiment) to +1 (very positive sentim... |
H: finding the mean for each of the channels (RGB) across an array of images
How can I find the mean for each of the channels (RGB) across an array of images?
For example train_dataset[0]['image'].shape is (600, 800, 3) and len(train_dataset) is 720 meaning it includes 720 images of dimension 600x800 and 3 channels. t... |
H: why R-square always keep increasing
I have read in multiple articles that R-square always increases with the number of features, even though a feature may not be of any significance.
The formula for R-square is
$$1 - \frac{\sum(y-\hat{y})^2}{\sum(y-\bar{y}^2)}$$
If the denominator is constant that means R-square is... |
H: How to choose variables to perform Exploratory Data Analysis
I have a dataset with about 110 variables. I have a target variable and I want to do an exploratory data analysis to find out what factors affect this target variable.
In such scenarios when we have a lot of variables,how do we choose which variables to a... |
H: Using pretrained LSTM and Bert Models in CPU Only Environment - How to speed up Predictions?
I have trained two text classification models using GPU on Azure. The models are the following
Bert (ktrain)
Lstm Word2Vec (tensorflow)
Exaples of the code can be found here: nlp
I saved the models into files (.h5) for la... |
H: For a linear model without intercept, why does the redundent term in one-hot encoding function as intercept?
In this question Elias Strehle pointed out that if we keep all the levels during one hot encoding on a linear model without an intercept, the redundant feature will function as an intercept. Why is this the ... |
H: Backpropagation and gradient descent
I just want to clear one doubt - we use gradient descent to optimize the weights and biases of the neural network, and we use backpropagation for the step that requires calculating partial derivatives of the loss function, or am I misinterpreting something?
AI: Yes you are corre... |
H: Tree-based algorithms and ordinal features
For tree-based methods (e.g., DT, Random Forest, Gradient boosting, etc.), does the conversion interval of an ordinal feature to continuous matter matters? (I can see why it matters for linear model, but I am not clear for tree-based methods)
For example:
Is there a differ... |
H: Subsequence parttern matching for time series
I have a set of time series data (just like voice sequence data) with the pattern as shown in the first figure (theoretical data). The measured data is given as presented in the second figure. What I want to do is localizing/finding the subsequent pattern as shown in th... |
H: What is the correct formula for Jaccard coefficient with integer vectors?
I understand the Jaccard index is the number of elements in common divided by the total number of distinct elements. But it seems to be some discrepancy or terminology confusion about Jaccard being applied to "binary vectors", meaning a vecto... |
H: ML to find relationship between a number and 50 other characteristics values
I have 10K records of football Players, their market value and about 50+ characteristics values of each player (i.e. Shot Power, Attacking, Defending, Position, Stamina and etc.) --> 80% are numerical values out of 100, 20% are categorical... |
H: Precision Accuracy and recall rate of a confusion rate of confusion matrix
Assume you are solving a 4-class problem. Your test set is as follows:
• 5 samples from class 1,
• 10 samples from class 2,
• 5 samples from class 3,
• 10 samples from class 4.
• Total Samples: 30
The decision made by your clas... |
H: Which ml framework/library makes you do the most of the work but still eases the process for you?
This may sound like a stupid question, but trust me it's not, I am searching for a ml framework/library that makes you do most of the work yourself so I can learn rather than depending on a framework/library but STILL... |
H: Machine learning algorithms for identification and classification of Microorganisms
https://www.google.com/search?q=Viruses+images&tbm=isch&ved=2ahUKEwiB9-fsoL3sAhUJyHMBHWRZB-sQ2-cCegQIABAC&oq=Viruses+images&gs_lcp=ChJtb2JpbGUtZ3dzLXdpei1pbWcQA1AAWABgo_sCaABwAHgAgAEAiAEAkgEAmAEAwAEB&sclient=mobile-gws-wiz-img&ei=HL... |
H: What is momentum in neural network?
While using "Two class neural network" in Azure ML, I encountered "Momentum" property. As per documentation, which is not clear, it says
For The momentum, type a value to apply during learning as a weight on
nodes from previous iterations.
Although that is not very clear. Can s... |
H: Correlation among features (e.g. doc length, punctuation, ... ) in classifying spam emails
I extracted some other features from my dataset regarding punctuation, capital letters, upper case words. I got these value:
looking at the correlation with my target variable (1=spam, 0=not spam), using .corr() in python.
B... |
H: Merging Training and Validation Sets for better accuracy
I am training an Image Classification Model and my train-test set distribution is 80-20.
After Training my train-test loss curve looks like this
As the model is converged after around 20-30 epochs and is not overfitting.
Can I also try to train the same mo... |
H: How to set a class_weight Dictionary for Random Forest?
I'm dealing with an unbalanced dataset, so I decided to use a weight dictionary for classification.
Documentation says that a weight dict must be defined as shown below:
https://imbalanced-learn.org/stable/generated/imblearn.ensemble.BalancedRandomForestClassi... |
H: How to get DATE column from dataframe in pandas without using CSV
This is my code.
import pandas as pd
import pandas_datareader.data as web
import datetime as dt
start = dt.datetime(2019, 2, 1)
end = datetime.today().strftime('%Y-%m-%d')
slug = "tsla"
df = web.DataReader(slug, 'yahoo', start, end)
print(df)
This... |
H: How does layer normalization work exactly?
As far as I understand, layer normalization normalizes across all the features for fully connected layers. Does that mean that for each batch dimension we have to learn the normalization parameters? Also, what do we do at test time if our batch dimension is different?
AI: ... |
H: how print f1-score with scikit´s accuracy_score or accuracy of confusion_matrix?
I would like to print the f1-score. I got confused about the wording f1-accuracy score and accuracy score. What is the difference of these 2 scikit-learn metrics and how can I print the f1-score out of this code?
from xgboost import XG... |
H: What does n means in neural network neuron output?
I've found this equation that explains the output of a neuron in a MLP network:
$y(n) = f(\mathbf{w}^T \mathbf{x}(n) + b)$
I can understand the general context, but since i have no background with mathematical notation, i don't understand what the $(n)$ parameter m... |
H: Does the output of the Sequence-to-Sequence encoder model exist in the same semantic space as the inputs (Word2vec)?
Does the output generated from the LSTM encoder module exist in the same semantic space as the original word vectors?
If so, say for example we have a sentence and we pass it through the encoder to g... |
H: How to choose between Tensorflow and Pytorch?
Recently I've been working on a pretty vanilla ANN model in Python with sklearn (and its preprocessing pipeline), mostly in jupyterhub notebooks if that matters.
I am considering changing the framework I work with as I need some more tools (drop out at least). but I am ... |
H: Scaling of variables considering the values of a single column or the whole dataset
I read many time that for machine learning and data mining algorithms the multi-dimensional input data should be scaled (e.g. normalized or standardized). Now my question is whether the average, min or max value shall be calculated ... |
H: Item-to-Item recommendation using DNN
I am new to DNN still learning, have a need to build item-to-item content based recommendation using DNN. For example, say I have a column of strings where each row represents a document I need to compute the cosine similarity of this column and recommend similar documents.
id ... |
H: I want to run PCA on a data set that will be aggregated by country. Should I aggregate the data before or after I standardize the data, and why?
Basically the title asks my question. I have the results of a survey that was filled out by people from different countries. I have been asked to analyze the data using PC... |
H: Effect of weights on the Louvain communities detected
The Louvain method for community detection aims to optimize modularity and hence detect communities in the given graph. In case of a weighted graph would it be valid to assume that an edge with a higher weight would be prioritized to be part of a community as ag... |
H: Performing a linear regression with Perceptron
I was wondering about the link between the linear regression and the perceptron!
Perceptrons were used as binary classifiers i.e to classify binary labels ( 0 or 1 ).
My question is How can you perform a linear regression with perceptron and what is the link?
I think t... |
H: Loading collections of datasets - Python code examples
Sometimes you might want to check your ideas on multiple datasets.
There are several places with datasets collections.
Question: Please share some Python scripts how to download multiple datasets from these (or other)
datasets collection ?
Ideally one should b... |
H: Including column of indices as predictor for model?
I have a small dataset consisting of 1000 observations (rows), 11 predictors + 1 response (12 columns). It is a toy dataset used for learning purposes in a machine learning class at university -- binary classification (heart disease vs. no heart disease).
I have f... |
H: Is there a RandomForest implementation that handles categorical data without encoding in python?
I am working on a binary classification project with both continuous and categorical features. I know that the R implementation of RandomForest can handle categorical data passed in as factor type data. Python's scikit-... |
H: Same confusion matrix when changing DecisionTreeClassifier parameters
I'm trying to build my first Decision Tree Classifier using the Iris dataset in the sklearn library. This is my first sample code:
from sklearn.datasets import load_iris
from sklearn.metrics import accuracy_score
from sklearn.metrics import f1_sc... |
H: How to determine layers structures appropriate for a specific application?
I'm playing around with Keras. I went through their example of MNIST data, cats vs dog etc.
The structure of the network they suggest is different in each case. The MNIST stucture:
# Build a simple model
inputs = keras.Input(shape=(28, 28))
... |
H: How to deal with NaN values after merging or joining two data frames?
After merging two dataframes, I end up with NaNs in the new dataframe, because one csv does not have all the ID's that the other has (Two dataframes of different sizes for example). So some rows have NaN values in some columns. Should I deal wit... |
H: What is the next step after k fold CV?
I came across this video lecture https://www.youtube.com/watch?v=wjILv3-UGM8
on k fold cross validation (CV). The algorithm given in the video lecture is presented below:
for k = 1:5
train on all except k
get model $M_{\tilde{k}}$
calculate accuracy on $k$ as $A_k$
end
Calcul... |
H: How much data do you need to build a classifier?
I would like to ask you what a good size of dataset would be for building a classifier.
I know that there are datasets of 1000 obs and datasets of 1m obs. But I also read papers where classifiers were built on datasets of 300 obs.
I think the size may affect the accu... |
H: Correctness of a ROC Curve
I've built a Decision Tree Classifier to practice with the sklearn library. My first task was to shuffle the iris dataset and split it keeping only the last 10 elements for the test. Then, after the training part I predicted the class of these elements and printed other useful metrics to ... |
H: How do I deal with additional input information other than images in a convolutional neural network?
I try to convert a game state of a board game into the input for a convolutional neural network. A convolutional neural network is useful because the players have to place items on the board, and the convolutional n... |
H: What does this formula in Glorot & Bengio mean?
In this paper, on page 5, we find the formula
$$Var(z^i)=Var(x)\prod_{i'=0}^{i-1}n_{i'}Var(W^{i'})$$
I am really struggling to understand what is meant by this formula. I think at least some of the following are true:
We're dealing with a linear neural network, i.e. ... |
H: How to achieve better accuracy of 90+ on a 3 class highly skewed dataset?
I have a 3 class dataset with very high imbalance classes:
class 1: 75000
class 2: 27000
class 3: 3000
With simple learning algorithms, accuracy is 84.6% but as expected mostly predicts class 1, few class 2 and no class 3.
With techniques, l... |
H: model.predict() accuracy extremely low on training dataset
This question is similar to this.
I'm new to ML, and I am trying to classify breast cancer histology images using EfficientNets with Transfer Learning. The dataset is small (400 images in total - there are 4 classes and all classes are equally balanced) and... |
H: Where are the 60 million params of AlexNet?
On the abstract of the AlexNet paper, they claimed to have 60 million parameters:
The neural network, which has 60 million parameters and 650,000 neurons, consists
of five convolutional layers, some of which are followed by max-pooling layers,
and three fully-connected l... |
H: What's the most effective way to measure the accuracy of my multi-class classification NN?
I'm relatively new to data science, and am confused about how to measure the accuracy of a multi-class neural network. The model I'm building is attempting to predict the outcome of a given event with 20 different outcomes - ... |
H: Interpreting Learning Curves
I'm curious on how to interpret the following learning curves, which can be considered an actual good learning curve of the three learning curves. I'm not sure when to consider if there is a high variance or bias, likewise the presence of underfitting or overfitting respectively. Thank ... |
H: Reuters Dataset Labels description
the list of Reuters dataset labels are provided with the dataset and also available in varous online resources. Here is an example.
But I couldn't find what each of these labels actually means.
I was able to find out 'acq' is short for 'Acquisitions'. But yet don't know these ones... |
H: Pandas loads a csv file incorrectly, but without throwing an error
So I tried loading some data through pandas to practice manipulating it, but I ran into a slight problem. Basically, the pandas load the data improperly. And it does so consistently. Let me show you what I mean.
This is a snapshot of the dataset (a ... |
H: How to normalize the data correctly in spam dataset
I'm working on the spam dataset to classify the inputs into binary classes.
My problem is that the observations in the dataset are float small numbers in the first 53 columns, and the 54 is float larger numbers, while the last two columns are integers.
My Questi... |
H: Understanding the generality of the NER problem
Named-entity recognition (NER) is a well-known problem in the NLP literature.
It typically addresses the problem to locate and classify named entities in text, e.g. Organizations and Products.
I'm trying to solve a similar problem but, in my view, a bit more general.... |
H: Why does my model produce too good to be true output?
I am trying to run a binary classification problem on people with diabetes and non-diabetes.
For labeling my datasets, I followed a simple rule. If a person has T2DM in his medical records, we label him as positive cases (diabetes) and if he doesn't have T2DM, w... |
H: Model Architecture Design
I have two different model architectures, I am just curious if one would be better then the other. I have tested both and they vary in performance each time I train them, but the second model generally performs a bit better, however not a major difference.
Question:
Are these models going ... |
H: How to represent genre or artist name in a neural network
I am writing a music recommendation system using machine learning. I'm attempting to make sense of ensemble networks to allow the system to learn from both the content-based features, as well as the global 'meta' features, such as the genre, year, artist etc... |
H: Bug in sentiment analysis and classification for unlabeled text
I'm working on the transcript of Trump and Biden's debate and want to analyze the sentences and classify negative, positive, or neutral comments, but I ran into one problem. I used both TextBlob and the transformers pipeline to analyze the sentiment bu... |
H: Hyperparameter tuning XGBoost
I'm trying to tune hyperparameters with bayesian optimization. It is a regression problem with the objective function: objective = 'reg:squaredlogerror'
$\frac{1}{2}[log(pred+1)-log(true+1)]^2$
My dataset consists of 20k vectors, each vector has length 12 (twelve features). Every vecto... |
H: Is active learning able to detect challenging cases?
Let's say we have a set of data points that need to be labelled for a classification task. In the pool-based active learning, if we go with the uncertainty measure, is the AL approach able to detect challenging cases? By challenging cases I mean samples that rece... |
H: Handling dimensions for RGB data with Keras CNN
I'm trying to make work the code from Keras' documentation getting started. There is something I do not understand about handling RGB data. I made one work with MNIST data (which is greyscaled), but I can't seem to figure out the CIFAR10.
HEIGHT = 200
WIDTH = 200
def ... |
H: accuracy at a false positive rate of 1%
I need to calculate the accuracy but at a false positive rate of 1%.
I am not sure if it is the normal accuracy that we can calculate with sklearn or I need a customized formula?
AI: Calculate this by finding the threshold at which the false positive rate is 1%. Your model ou... |
H: what is criterion in flcuster of scipy package?
Could some one explain what does criterion of fcluster indicate? I tried to read the documentation but I am unable to understand. What does maxclust criterion indicate?
AI: Welcome to the community!
You may want to refer to a tutorial on Agglomerative Hierarchical Clu... |
H: Normal distribution and Random Forest
I have big table in dataframe (600k rows) which has y column (the variable I want to predict) and other 4 other columns that are the X.
I have run RF regressor and I got score of 0.87 when I run it on the train and test.
However, when I tried to predict another set of data (w... |
H: Stemmer or dictionary?
I have recently ported a stemmer from Java to Python for a highly inflectional language.
The stemmer learns how to change suffixes from the dictionary of words and their inflected forms. It basically builds a stemming table with learned stemming rules. As I was porting the algorithm I decided... |
H: Machine Learning validation data returns 100% accuracy
I'm Testing a Machine Learning model with validation data returns that return 100% correct answers, is it overfitting or the model works extremely well, do I need to continue training on more data?
I'm not sure how to interpret the result, any guidance please?
... |
H: DecisionTreeRegressor under the hood of GradientBoostingClassifier
I'm inspecting the weak estimators of my GradientBoostingClassifier model. This model was fit on a binary class dataset.
I noticed that all the weak estimators under this ensemble classifier are decision tree regressor objects. This seems strange to... |
H: Can I fine-tune the BERT on a dissimilar/unrelated task?
In the original BERT paper, section 3 (arXiv:1810.04805) it is mentioned:
"During pre-training, the model is trained on unlabeled data over different pre-training tasks."
I am not sure if I correctly understood the meaning of the word "different" here. differ... |
H: How to train a model on top of a transformer to output a sequence?
I am using huggingface to build a model that is capable of identifying mistakes in a given sentence.
Say I have a given sentence and a corresponding label as follows ->
correct_sentence = "we used to play together."
correct_label = [1, 1, 1, 1, 1]
... |
H: add labels bar chart
There is this dataframe:
print(df)
df.plot.bar(figsize=(10,5),fontsize=14,)
plt.title('counts',fontsize=15)
plt.show()
barh:
df.plot(kind='barh')
How can I add value labels on this bar and barh chart?
AI: Unfortunately, adding text to the plot must be done manually. See the example below.... |
H: SMOTE train test split with validation data
Would like to ask, in which way to use SMOTE?
My dataset is imbalanced and a multiclass problem. As I read in many posts, use SMOTE method only for the training dataset (X_train and y_train). Not for the test dataset (X_test and y_test). There I include validation data. H... |
H: Books about statistical inference
I'm currently taking a course "Introduction to Machine Learning" which covers the following topics:
linear regression, overfitting, classification problems, parametric & non-parametric models, Bayesian & non Bayesian models, generative classification, neural networks, SVM, boosting... |
H: How to train with cross validation? and which f1 score to choose?
I got similar results in 2 models which consists of similar algorithms.
Model 1 with cv=10 has a f1'micro' of 0.941. See code below.
Model 2 only train test split (no cv) has f1'micro' 0.953.
Now here is my understanding problem. Before I did a Grid-... |
H: Impossible to increase model accuracy
I'm building binary classification models on my company's dataset. The problem I'm having is that I haven't been able to increase the accuracy of my models. I have trained, tuned, cross validated models such as logistic regression, knn, neural networks and they all have results... |
H: Emotion detection on audio
I'm trying to apply emotion detection on audio. Basically, given an audio file, it must generate emotional labels for a segment of any durations, say every 5s chunks, like happy, sad, angry, etc.
Is there a working example with pretrained models available to test? I was looking for a samp... |
H: Dividing data set into (almost) equal batches based on characteristics
I have a large dataset containing 6 characteristics (all numerical).
I need to split this dataset into multiple batches to be processed in parallel, and ideally, the batches should be as equal in size as possible.
The catch is: I can only split ... |
H: Calculate the number of params of a neural network
I'm not clear how the number of params on my Convolutional Network in keras is estimated.
Could you help me to understand this?
Model: "sequential_7"
_________________________________________________________________
Layer (type) Output Shape ... |
H: Confusion matrix in sklearn
If you look at this:
>>> y_true = ["cat", "ant", "cat", "cat", "ant", "bird"]
>>> y_pred = ["ant", "ant", "cat", "cat", "ant", "cat"]
>>> confusion_matrix(y_true, y_pred, labels=["ant", "bird", "cat"])
array([[2, 0, 0],
[0, 0, 1],
[1, 0, 2]])
I suppose fist row of array me... |
H: SelectFromModel vs RFE - huge difference in model performance
Note: I have already looked at Difference between RFE and SelectFromModel in Scikit-Learn post and my query is differnt from that post
Expectation:
SelectFromModel and RFE have similar/comparable performance in the model built using their recommendation... |
H: How to maximize recall score for specific label in multiclass classification?
Thought I had solved the problem but I'm having inconsistent issues with it so reaching out here.
I have a multilabel classification problem with four labels ['--','-','+','++'] and with a basic random forest model, I have significant per... |
H: How to study the effect of eps in sklearn.cluster.DBSCAN?
I posted this question on stackoverflow.com and have not received any answer. In case I get an answer from one of them, I will inform on the other.
I have a dataset and is requested by my professor to study the effect of eps in sklearn.cluster import DBSCAN... |
H: Unable to make CNN model with same output and input dimensions
I'm trying to develop a simple CNN model that takes in a RGB images and returns the same 3 channel as output.
Eg: 3 x 128 x 128 is what I'm giving as input
K.set_image_data_format('channels_first')
def resBlock(x, channels, kernel_size=[3, 3], scale=0.1... |
H: Extract names from email address
Say I have two email addresses and I would like to see if it is likely that they belong to the same person. For example, Cameron_M_Thompson@company.com and cthompson1024@personal.com is likely to be from the same person (it doesn't have to be certain, providing the likeliness would ... |
H: how to create multiple subplots( scatterplot) in for loop
I want to create scatterplot of target variable with all the other variables in dataframe. Just like we create pairplot but as pairplot with large number of variables will take lot of time and computing memory, it is required to draw scatter plot containing ... |
H: Convert Neural network to Keras Classifier
I am training a Neural Network for Multi-Class classification. After successfully training it and validating the model through cross-validation, I would like to use this network inside a voting Classifier. In order to perform cross-validation on my trained network, I conve... |
H: Why the accuracy of my bagging model heavily affected by random state?
The accuracy of my bagging decision tree model reach up to 97% when I set the random seed=5 but the accuracy reduce to only 92% when I set random seed=0. Can someone explain why the huge gap and should I just use the accuracy with highest value... |
H: Running scikit-learn with large volume
I need to run a Random Forest process with scikit-learn. To train the model, I have a database table with 10 million rows of features. The question is: what is the best way to approach this, should I load into memory the 10 million rows, for example with numpy or pandas or the... |
H: finding similarity of a new datapoint
I have built a recommendation engine using cosine similarity. When I want to find all the records similar to a given record that is already present in the dataset it works. Consider a case, a user enters a new record which does not exist in the similarity matrix, to find and re... |
H: Need to kickstart learning rates
I was just looking at the docs on Pytorch for different available schedulers and I found one that I am having some trouble understanding here.
The others seem to make sense: As training progresses, the learning rate gradually decreases. But in my study so far, I am yet to come acro... |
H: Scaling continuous data to discrete range
Edit: the context is as follows: I've trained some ML model that predicts some feature vector. Thats a. But I know that a can take some values from discrete range, but the model outputs continuous values. After applying predict I want to enforce the outputs to take the disc... |
H: Compare cross validation and test set results
I am having a hard time understanding the results of a cross validation test and a test run on a test set.
First I made the following pipeline:
pipe=Pipeline([('clf',DecisionTreeClassifier(random_state=0))])
Then I use cross validation on a scaled training set(75% of th... |
H: R code that gives results like Wolfram Alpha for the expectation of a function of a random variable?
When I ask Wolfram Alpha to calculate $E[f(X)]$ where $f(x) = e^{-x^2}$ and $X \sim \mathcal{N}(1,4)$, it gives the result
$$
E[f(X)] = \frac{1}{3\sqrt[9]{e}} \approx 0.29828,
$$
and the following plot which appears... |
H: Should I include all dummy variables or N-1 dummy variables (keep one as reference) in neural networks
I have a categorical variable with N factor levels (e.g. gender has two levels) in classification problem. I have converted it into dummy variables (male and female).
I have to use neural network (nnet) to classif... |
H: Model inclines because of imbalanced data
In life, some events are rare and most cases are normal. So I am wondering, to detect the rare cases, shall we use an imbalanced dataset with more historical rare cases?
Taking the German Credit Data, as an example. It contains data on 20 variables and the classification wh... |
H: Training, Testing and Validation Dataset
I'm training a Unet model for tumor segmentation.
I have a dataset of 400 patients for that.
The used images are CT scans (3D images) that I divide into 2D images (for a total of 30k 2D images).
I am actually splitting the dataset into:
10% test data, 18% validation data, 72... |
H: How to sample with guaranteed minimum spacing?
I have a timeseries stored as a pandas.Series, and have computed the autocorrelation to be negiligable for 12 hours lag. Therefore, I want to test the approach of sampling data points randomly, but with at least 12 hours between them, and then treating them as IID data... |
H: Maximum Likelihood with Gradient Descent or Coordinate Descent blows up
Context
The maximum likelihood estimators for a Normal distribution with unknown mean and unknown variance are
$$
\widehat{\mu} = \frac{1}{n}\sum_{i=1}^n x_i \qquad \text{and} \qquad \widehat{\sigma}^2 = \frac{1}{n}\sum_{i=1}^n (x_i - \mu)^2
$$... |
H: How to create a model to suggest similar words in realtime?
I have a huge database of job titles, I want to build a system where if you enter something like "jav " then it should suggest next some similar job titles like (java developer, java engineer) etc..
How should one approach this problem? How can build somet... |
H: Which probability distribution will you use to model outliers?
I was asked this question in a recent interview for the position of a Data Scientist: Which probability distribution will you use to model outliers ?
I told him outliers are like rare events which can be modelled by a Poisson distribution. I pretty sur... |
H: Is the result of feature extraction a feature representation?
If a use a feature extraction method on images, do I then get a feature representation or is there a different meaning behind feature representation?
To my understanding, when I use a CNN on an image the convolutional layers perform feature extraction. C... |
H: Where is the rotated angle actually located in fitEllipse method?
Main task: I fit the ellipse using the fitEllipse() method and then I'd like to count the rotation angle between the horizontal axis and the major axis of the generated ellipse. I'm going to do this using the 3rd returned argument from the fitEllipse... |
H: What is a distribution-wise asymmetric measure?
I was trying to understand KL-Divergence, $$D_{KL} \langle P(X) \Vert P(Y) \rangle,$$ and was going through its Wikipedia article. It says the following
In contrast to variation of information, it is a distribution-wise asymmetric measure and thus does not qualify as... |
H: Problem importing imblearn
I am trying to do:
import imblearn
in my jupyter notebook, but when I run the cell I get the following error message:
SyntaxError: invalid syntax
even if I have installed the package. I am using python 2.
What could the problem be, and how do I solve it?
AI: If you check the pypi source... |
H: What is Deep supervision?
I'm interested in segmentation models for medical imaging purposes.
When I looked at the state of the art, I fell on a paper on a new architecture, Unet++:
UNet++: A Nested U-Net Architecture for Medical Image Segmentation from Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, a... |
H: Fitting probabilities in scikit-learn RandomForestClassifier
This is a newbie questions, so please bear with me. Given this random forest model:
from sklearn.ensemble import RandomForestClassifier
X = [ [2,1,1,1], [2,0,2,1], [3,1,1,1] ]
y = [ 0, 1, 2 ]
regr = RandomForestClassifier(n_estimators=200, max_depth=5)
... |
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