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H: How to process Dicom Images for CNN?
I am building a disease classifier. I have Dicom scans for many patients.
The scans have different slice thickness, and different scans have different number of slices. However, the slice thickness for a single patient's scan has the same thickness for all slices.
For example:
... |
H: Skin Detection Classifier
I have a small data set containing around 80 images for people, and the corresponding ground truth for skin regions. I want to train a classifier to be able to detect a skin, and use it later on my own generated data, but I'm not sure what is the right way to go. I believe it would be hard... |
H: Fitting glm without explicit declaration of each covariate
When I fit a linear model with many predictor variables, I can avoid writing all of them by using . as follows:
model = lm(target_deathrate~., data = full_data)
But for models with higher complexity, I cannot make this work:
x = glm(target_deathrate~poly(.... |
H: Risk prediction vs classification model
I am working on a binary classification model. Currently, when I use scikit logistic regression, it outputs binary values like 0s and 1s. However, I understand, from online reading, that it outputs probability, and based on threshold of 0.5, converts them into two classes.
... |
H: What inference can we draw from the frequency distribution of thresholds?
I've the probability scores of positive class of two models. The frequency distribution of those probability scores(thresholds) are like this
Model #1
Model #2
It's a binary classification problem. And the thresholds obtained are from the ... |
H: data splitting into 3 sets based on years
let's suppose we have a customer data from the year 2015 to 2019, I want to train_test_split() my data such that my data gets divided into three sets, set-1 is from 2015 to 2017 (3 years) on which i will train my model, set-2 i.e. 2018(1 year) on which i will validate my mo... |
H: Activation function between LSTM layers
I'm aware the LSTM cell uses both sigmoid and tanh activation functions internally, however when creating a stacked LSTM architecture does it make sense to pass their outputs through an activation function (e.g. ReLU)?
So do we prefer this:
model = LSTM(100, activation="relu"... |
H: Synthetic time series generation according to some distribution
I'm trying to develop a change detection model that uses sliding windows. Given a time series with some features I've a sliding widows that analyses that time period and compares with a successive time window. If the distribution of data has changed th... |
H: How to plot multiple columns with ggplot in R?
I do have a data frame with different categorical and numerical columns with the following schema:
Id | num_col_1 | num_col_2 | num_col_3 | cat_col_1 | cat_col_2
Now I want to draw a combined plot with ggplot where I (box)plot certain numerical columns (num_col_2, nu... |
H: Justification for values used in backpropagation
I'm learning the method for backpropagation in adjusting weights. A generalization of a formula used to determine the change made to a respective weight is
where is the rate the total error changes as the i-th weight changes. I get why this value is interesting in ... |
H: Knowing Joint probability distribution between feature-label space
I am doing a course CORNELL CS4780 "Machine Learning for Intelligent Systems". you can find the link here for the one I am going to refer 1st lecture
The professor explains, we have a sample
$D ={ (X_1,y_1),(X_2,y_2), \ldots,(X_n,y_n)} \sim P$
... |
H: Handling Categorical Features on NGBoost
Recently I have been doing some research on NGBoost, but I could not see any parameter for categorical features. Is there any parameter that I missed?
__init__(self, Dist=<class 'ngboost.distns.normal.Normal'>, Score=<class 'ngboost.scores.MLE'>, Base=DecisionTreeRegressor(c... |
H: Interpreting Gradients and Partial Derivatives when training Neural Networks
I am trying to understand of purpose of partial differentiation in NN training by knowing how to interpret gradients and their partial derivatives. Below is my way of interpreting them so I would like to know if I am correct, and if not, c... |
H: Trying to return more than just the top result from sklearn NearestNeighbors
I'm trying to compare a list of names (duplicated into a clean file and a messy file). I then compare the files against each other. My problem is that it returns only the top 1 result for each, which is itself (the identical record in ea... |
H: Modifying a distribution by adding in samples incrementally
I would like to calculate the distribution (e.g., Gaussian) of a set of samples. However, I would also like to see how the distribution changes as I fit the samples into the distribution incrementally.
One way to do this would be to compute the distributi... |
H: How to properly apply CrossValidation and/or split the dataset?
I have a particular problem and do not really now how to properly validate my experiments in this scenario.
There is one big data set with 100.000 samples, 99.000 y=0, 1.000 y=1
Each sample has 1.000 Features
There are 10 different subsets of feature ... |
H: How to validate regex based Resume parser efficiently
I am using rule based logic to extract features from resume. Basically I am trying to find if the candidate switched the company in less than 1 year. So I have the code in place to find it using python. However if I want to validate it, I am currently doing it m... |
H: Does it make sense to do train test split when trainning GANS?
For normal supervised learning the dataset is split in train and test (let's keep it simple).
Generative Adversarial Networks are unsupervised learning but there is a supervised loss function in the discriminator.
Does it make sense to split the data i... |
H: Comparing excel data sets in Pandas
Pretty new to Python, but as an SEO I'm looking at the benefits of using notebooks in my workflow.
I've got two excel files which I've cleaned and imported into a new notebook using pandas.
I'm trying to compare position changes and create a new dataframe with new columns to sh... |
H: What applications does linear programming have in data science?
I'm currently learning about linear programming in my degree. I'm wondering how this is relevant to anything in data science?
AI: Whenever you have an optimization problem the first question that you have to ask yourself is.
Can I make it a Linear Prog... |
H: Consider ratings as sentiment labels?
Beginner here!
I have a dataset, with reviews of a product as text, ratings for the product.
My previous motive was to use Naive Bayes classifier for sentiment analysis. But my data doesn't have the variables( sentiment) required - negative/positive.
Shall I use the ratings (... |
H: Reason for generally using RMSE instead of MSE in Linear Regression
In linear regression, why we generally use RMSE instead of MSE? The rationale I know is that it's easy to minimize the error in RMSE instead of MSE by Gradient Descent, but I need to know the exact reason.
AI: However RMSE seems similar to MSE and ... |
H: How to choose the best parameter values for TfidfVectorizer in sklearn library?
Recently, I used TfidfVectorizer in scikit-learn library to calculate a matrix of TF-IDF features. However, I do not know how to set some parameters such as max_features, min_df, max_df, etc.
AI: I think these parameters are mostly use... |
H: "Pip install tensorflow" only installs version 1.14 – how to install version 2 (with eager mode enabled?)
The question is already in the title: when installing tensorflow in command line (in a virtual environment or not...), it is always tensorflow version 1.14.0 which is going to be installed. Consequently, eager ... |
H: Decision tree regression: Polynomials unnecessary?
I am testing out different models for a regression task.
When using OLS, Ridge and Lasso, I use different polynomial degrees of the explanatory variables.
Example: For two variables x and y, degree 2 would give the explanatory variables x, x^2, xy, y, y^2.
When us... |
H: Assistance needed on what machine learning approach to use
I'm currently writing my Master's Thesis on Subjective tagging of sounds and I feel that I've been stuck with the same problem for quite a time now and need assistance to progress. I'll, in short, describe my goal and what I've accomplished so far.
I'm wri... |
H: What is a 'hidden state' in BERT output?
I'm trying to understand the workings and output of BERT, and I'm wondering how/why each layer of BERT has a 'hidden state'.
I understand what RNN's have a 'hidden state' that gets passed to each time step, which is a representation of previous inputs. But I've read that BER... |
H: RandomForest and tree feature importance in scikit-learn
What is the difference between model.feature_importances_ and tree.feature_importances_ in the following code:
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
# Boston Housing datase... |
H: Understanding cosine distance with word vectors
I'm a new DL4J user, and I'm running all the works of Shakespeare through a Word2Vec neural net. I've got a pretty basic question about how to understand the results so far. In the below example, there's an obvious association with the "ing" in king and the "ing" in o... |
H: Keras RNN (batch_size
I created an RNN model for text classification with the LSTM layer, but when I put the batch_size in the fit method, my model trained on the whole batch instead of just the mini-batch _size.
This also happened when I used GRU and Bidirectional layer instead of LSTM.
What could be wrong?
def cr... |
H: h in LSTM increasing in size?
So I was reading about the LSTM architecture and I was having trouble understanding a certain aspect of it. This article mentions the step in question near the bottom of the page. Here is the image given:
The issue I have is this: If ot is the result of an operation on the concatenatio... |
H: Train-Test split for a recommender system
In all implementations of recommender systems I've seen so far, the train-test split is performed in this manner:
+------+------+--------+
| user | item | rating |
+------+------+--------+
| u1 | i1 | 2.3 |
| u2 | i2 | 5.3 |
| u1 | i4 | 1.0 |
| u3 | ... |
H: If a dataset is imbalanced in real life, should I train on my machine learning model on an imbalanced dataset
I have a dataset where around 20% of the data is the positive class and 80% of the data is the negative class. When I undersample and train my classifier on a balanced dataset and test on a balanced dataset... |
H: How to deal with with rows with zero in every feature while clustering?
I am working on a clustering problem which has 13000 observations and 15 features. Around 3000 observations in the dataset has zero in every features ( i.e all values zero in 3000 rows). I am trying to do clustering on top of it. What is a bet... |
H: On regression to minimize log distance rather than distance
Suppose I have a lot of points $ x_i \in \mathbb{R}^N $ with corresponding non-negative labels $ y_i \in \mathbb{R} $ and I want to do regression and make a prediction on some new datapoint $ x^* \in \mathbb{R}^N $ for which I don't have a label. Is ther... |
H: Make a random forest estimator the exact same of a decision tree
The idea is to make one of the trees of a Random Forest, to be built exactly equal to a Decision Tree.
First, we load all libraries, fit a decision tree and plot it.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use(... |
H: How to keep the test data from leaking into the training process of a machine learning algorithm?
I read in many different sources that I need to split my data into a training set and a test set. Then I have to make sure that the algorithm is trained only on the training data, and do my best to keep the test data f... |
H: Explainable AI and unsupervised algorithms
There are several packages that allow explaining ML algorithms (Lime, Shap and so on). However, it is not clear how we can explain unsupervised algorithms for example, if we use PCA for dimensionality reductions first and random forest after, how it is possible to explain ... |
H: Why is my validation loss going up while my validation accuracy also goes up?
Scenario:
I've been training a CNN for the cifar10 dataset.
I'm using tensorflow, and a CNN with 12 conv layers and 1 dense layer before a softmax dense layer.
I'm using data augmentation as well with batch normalization.
After a few hu... |
H: Why does my GridSearchCV always break up?
GridSearchCV for my Random Forest breaks up. I need to know the reason and the solution to make it work:
# Grid-Search for Random Forest
param_grid = {
'bootstrap': [True],
'n_estimators': [100, 200, 300, 400, 500],
'max_depth': [50, 100, None],
'max_feature... |
H: How does two convolution layers make computation quadratic increase in GoogLeNet?
In GoogLeNet papaer, "For example, in a deep vision network, if two convolutional layers are chained, any uniform increase in the number of their filters results in a quadratic increase of computation."
But, I don't know how a quadrat... |
H: Practical way to convert jupyter notebook to MS Word document?
What would be a practical way to convert a Jupyter Notebook to a Word document (.doc) ? I am asking this in a professional context, so I'd like to avoid manual solutions, do it in an efficient way (fast), avoid third parties... etc. Something that works... |
H: What level of math is required for machine learning research
There are several levels of math understanding:
Know the math
Know the intuitions behind math concepts
Know the intuitions and proofs of math concepts
Know the intuitions, proofs of math concepts and be able to apply them to deduce new results
My questi... |
H: Dealing with low-information centroids using Nearest Centroid Classifier and bag of words method
I am currently working on a problem where we have projects and e-mails that belong to a single project each.
My goal is to create a recommendation system for incoming e-mails which presents the projects the e-mail migh... |
H: Search for hyperparameters whith different features using Random Forest
I have a dataset in which I would like to perform a classification model, so I have decided to use Random Forest. The number of features that I have is approximately 200 and I would like to test which set of features gives me a better result. F... |
H: Should Feature Selection processes be apply on training data or on all data?
I've realized that on examples and guides, sometimes feature selection processes (correlation elimination, backward/stepwise) are applied on the train data after splitting all data but on the other hand, sometimes its applied on all data.
... |
H: Loss being outputed as nan in keras RNN
Since the first Epoch of the RNN, the loss value is being outputted as nan.
Epoch 1/100
9787/9787 [==============================] - 22s 2ms/step - loss: nan
I have normalized the data.
...,
[9.78344703e-01],
[1.00000000e+00],
[9.94293976e-01]]])
Exampl... |
H: Compare Coefficients of Different Regression Models
in my project, I am using asuite of shallow and deep learning models in order to see which has the best performance on my data. However, in the pool of shallow machine learning models, I want to be able to compare the coefficients of each regression model between ... |
H: Duplicated features for gradient descent
Suppose that our data matrix X has a duplicated column, i.e, there is a duplicated feature and the matrix is not full column rank. What happpens?
I guess that we can not find a unique solution because that's the case for the close form in linear regression, but I do not see ... |
H: Feature Importance from GridSearchCV
I created a GridSearchCV for a Random Forest Regressor. Now I want to check the feature importance. I searched around and I found this:
rf_gridsearch.best_estimator_.named_steps.feature_importances_
This already works, but my training data is huge, 669 attributes. Therefore, I ... |
H: Is there a link between Training, Test errors based on k fold CV and not doing CV?
I am using Matlab to train a feedforward NN using Cross validation (CV) approach. My understanding of CV approach is the following. (Please correct me where wrong)
Let X be the entire dataset with Y as the label set. Split X into 90... |
H: What are some best Text Representation techniques in NLP
I've studied about various text representation techniques like : Bag of Words, N-gram data modelling, Tf-idf, word embedding etc.
I would like to know which among all the techniques are most efficient when it comes to data modelling or representation for a su... |
H: validation accuracy and loss increase
I am training a generic LSTM based autoencoder to get the sentence embeddings, the bleu score is the accuracy metric. The model is coded to output the same number of tokens as the length of labels, hence the losses are calculated using cross-entropy loss between the output of t... |
H: A bump in CV score curve! What does it mean?
My learning curve is behaving strangely and I don't know if I'm doing anything wrong or it is because of the dataset nature.
I'm using a Neural network with (30,30,15,1) layers and 'ReLU' activation function for training and linear for the output layer.
My learning curve... |
H: Word Embedding or Hash?
In my dataset I have a 'text' column and a 'followers' column containing lists of follower IDs, i.e. '1093777852477116417, 936194589043683328,...'. Some of the 'followers' values contain thousands of IDs.
I am preprocessing the data for LSTM, and I will do word embedding on the text column.
... |
H: Passing data to SMOTE after applying train/test split
I'm trying to resample my dataset after splitting it into train and test partitions using SMOTE. Here's my code:
smote_X = df[cols]
smote_Y = df[target_col]
#Split train and test data
smote_train_X,smote_test_X,smote_train_Y,smote_test_Y = train_test_split(smot... |
H: Predicting parallel time series with multiple features
I am trying to predict sales for 2 departmental stores which share similar demographic properties. My goal is to make a single LSTM model to predict sales from these parallel time series having multiple features.
My input features for training would be
+-------... |
H: How to approach a new data set with no dependent variable
Basically I am taking a small course in data science.
And one of our first assignments are that we have gotten a pretty huge data set (hundred of thousands of samples, and roughly 150 independent variables). There is no response variable in this, but the ass... |
H: Unable to save the TF-IDF vectorizer
I'm workig on multi-label classification problem. I'm facing issue while saving the TF-IDf verctorizer and as well as model using both pickle and joblib packages.
Below is the code:
vectorizer = TfidfVectorizer(min_df=0.00009, max_features=200000, smooth_idf=True, norm="l2", \
... |
H: What does 'lm' mean in the lmplot() in seaborn?
I was learning seaborn, but could not find the full name of seaborn.lmplot. What is 'l' and 'm' in the name 'lmplot'.
AI: "lm" = "linear model". As reported in the docs:
Plot data and regression model fits across a FacetGrid. [...] It is intended as a convenient inte... |
H: Pandas dataframe, create columns depending on the row value
I get a csv that if you read it, looks like:
import pandas as pd
df = pd.DataFrame([['de,ch,fr', '1,2,3'],['fr,ch,dk', '3,4,5']], columns=['countries', 'numbers'], index=['abc', 'bcd'])
I want to make it look like this:
df = pd.DataFrame([[1,2,3,0], [0,4,... |
H: Several independent variables based on the same underlying data
I got a data containing, among others, two feature variables, which are based from the same underlying data (i.e. have mutual information), but they convey different information/message. How to handle such cases?
Since, logically, they will be highly c... |
H: Combine 100 2D arrays into one 3D array
I have 100 2D lists a0 to a99, it is easy to convert them into arrays (I verified already):
print(np.array(a0).shape) # (2,150)
print(np.array(a1).shape) # (5,150)
print(np.array(a2).shape) # (10, 150)
# the sizes of a0 to a99 vary from (1,150) to (10, 150) only
I want... |
H: What is the layer above/below in a NN?
In the lecture notes of CS231n, it says (emphasis mine)
... There are three major sources of memory to keep track of:
From the intermediate volume sizes: These are the raw number of activations at every layer of the ConvNet, and also their gradients (of equal size). Usually,... |
H: How to compute modulo of a hash?
Let's say that I have a set of users in my database, that have GUIDs as their IDs. I use xxhash to generate fixed-length hashes for each value, so that I can then proceed to "bucketizing" them and being able to do random sampling with the help of the modulo function.
That said, if I... |
H: Word2Vec and Tf-idf how to combine them
I'm currently working in text mining ptoject I'd like to know once I'm on vectorisation. With method is better.
Is it Word2Vec or Tf-Idf ?
Here I see we can combine them why that? Does it improve quality of data?
What about GloVe?
Thanks
AI: Word2Vec algorithms (Skip G... |
H: Dynamic pricing with aggregate constraints
so I have this situation. I am trying to understand whether my customers will buy my product at a certain price based on a previous offer made to them. Specifically, I have a lot of data on my clients and I have rolled a previous "campaign" round and I saw who bought it an... |
H: Recommender system that matches similar customers with similar highly rated products?
I have a dataset of 1,000 customers that bought 20 distinct phones and rated them 1-5. I have several demographic attributes for these customers (gender, age). My website offers 100 distinct devices, each with several attributes (... |
H: How to analyse player and enemy position for data analysis
I am trying to analyse a Serious Game for students learning.
In one of the game levels,
There are multiple positions like Player Position (x, y, z), enemy position (x, y, z), Player shot at position (x, y, z).
Other features like items acquired,
How do I u... |
H: What's the purpose of padding with Maxpooling?
As mentioned in the question, i've noticed that sometimes there are pooling layers with padding.
More specifically, I found this Keras tutorial, where there's a net which contains MaxPooling layers with padding.
If padding=same in convolutional layers, our output size ... |
H: matplotlib graph to plot values and variance
I am really new to the world of matplotlib graphing as well as using those graphs to understand data.
I have written a simple python code where I read a .csv file in and then store the values of one column into a variable. Then plotting them similar to the code bellow:
d... |
H: How to correctly calculate average F1 score, precision and recall of a Named Entity Recognition system?
My Named Entity Recognition (NER) pipeline built with Apache uimaFIT and DKPro recognizes named entities (called datatypes for now) in texts (e.g. persons, locations, organizations and many more).
I have a gold ... |
H: What does these points mean in Naive Bayes?
I have two concept related questions related to Naïve Bayes.
Naïve Bayes is robust to irrelevant features. What does this mean? Can anyone give an example how does the irrelevant features cancels out and what are the irrelevant features?
It is optimal if the independence ... |
H: Fitting a model as a significance testing alternative
My colleagues use somewhat unusual approach to estimate how far away performance of some of our company related processes diverge from historical levels. Their nature isn't really relevant for the purpose of this discussion. We may just assume here, that the fin... |
H: Why this calculation of weight vector in linear regression is only for small dataset?
Slides from my university says, that the following way of calculating the weight vector is suitable only for small datasets. Can you please explain, why it may be suitable for small datasets?
Here, X is observed variable and Y is ... |
H: How is the target_f updated in the Keras solution to the Deep Q-learning Cartpole/Gym algorithm?
There's a popular solution to the CartPole game using Keras and Deep Q-Learning:
https://keon.github.io/deep-q-learning/
But there's a line of code that's confusing, this same question has been asked in the same article... |
H: Classify graphs using machine learning
I am trying classify an input graph (a 2D point sequence) into one of the predefined graphs (A,B,C etc) using machine learning. The goal is to identify which type of graph the input graph belongs to.
I have done classification of single data points before, but I have never cl... |
H: How to support a dynamic shape input for tf.where()
Tensorflow (tf.where) function does not support dynamic input shapes.
For example, the following function calculates the svd of a matrix A and tries to identify the singular values greater than a certain threshold to invert them while filling other values with zer... |
H: Dealing with issues in "test" predictons for single "items" (null values, standardization in place, etc)
I know this is kind of a broad question but I have tried to scour both this forum and the internet in general to no avail for this particular situation. So imagine I have a model trained for which, though the da... |
H: Is it possible to pass dataframes between R and Python using Arrow with zero-copy?
Arrow is an in-memory format, so I can have a dataframe in Python backed by the arrow format. So the dataframe sits in memory, can I use that dataframe directly from R without making a copy of the dataframe? Imagine if the dataframe ... |
H: Will Keras fit( ) function automatically shuffles the input dataset by default?
I am asking this model fit( ) function.
fit(x=array_x, y=array_y, batch_size=32, epochs=10)
The question is straightforward:
Whether fit( ) will automatically shuffle the input dataset? (array_x, array_y in this case)
I ask this questi... |
H: How to split data into 3 parts in Python - training(70%), validation(15%) and test(15%) and each part have similar target rate?
I'm working on a company project which I will need to do data partition into 3 parts - Train, Validation, and Test(holdout).
Does anyone know how I can split the data into 3 parts above a... |
H: K-Means Clustering for data points with multiple attributes
I'm very new to K-Means clustering. Every example that I have seen has a two-dimensional data set.
I am working to classify recipes of varying ingredient composition into families. Each recipe is composed of a number of ingredients, and I want to group to... |
H: Clustering data set with multiple dimensions
I have a data set which is similar to the following:
It is recipe data along with the composition of the recipe (in %)
I have 91 recipes and 40 ingredients in total. I want to be able to cluster these recipes together into families based on similarity of ingredient com... |
H: Can numerical discrete finite data be always treated also as categorical?
In many sources, for example here data is classified as being qualitative (categorical) and quantitative (numerical). Where numerical data can be continuous or discrete, and discrete can be finite or infinite.
I want to establish if a numeric... |
H: Simulating mental disorders in machine learning systems
Is there any work as the following example in ML:
Suppose a reinforcement learning system which has a fixed penalty for every action, so there would be a regret for taking action if the estimated reward is wrong most of the time. And suppose the estimated acti... |
H: Matching Algorithms in Python
We run a online marketplace for Commercial Real Estate industry and are looking to write matching algorithms to reduce the cost of search and transaction for the property owners/tenants.
We have two groups of users - owners and tenants and would like to implement matching algorithms b... |
H: Loading models from external source
I have a 500MB model which I am commiting to Git. That is a really bad practice since for newer model versions the repository will be huge. As well, It will slow down all builds for deployments.
I thought of using another repository that contains all the models and then fetch the... |
H: What should I do with a test and train set not having same classes?
Assume we have a train dataset with 5 classes a,b,c,d,e but the test dataset have only d and e with extra class f not present in train set. If i want to do machine learning with this datset in weka or python what should I do with this dataset? shou... |
H: Getting feature vectors from CatBoost pool
I have a dataset with some numerical and categorical features and I am trying to apply CatBoost for categorical encoding and classification.
Since my dataset is highly imbalanced, with a large number of data samples with label 0 compared to those with label 1, I'm also try... |
H: How to select one record from multiple record for a subject during analysis?
I have a dataset where I am working on a binary classification. I have two classes of subjects. One is Outpatients and Other is Inpatients. (66:33 is the class proportion)
My objective is to identify the risk factors that influence hospita... |
H: Why i am getting t statistics different
import scipy.stats as st
st.t.ppf(0.95, df=9)
Output: 1.8331129326536335
As per this, I should get 2.262 with dof = 9.
AI: t.ppf is calculating a 1-tail inverse cdf. It looks like you're trying to look up the t-value for p=.95, but the value you are referring to in the table... |
H: How to use the $\chi^2$ test to select the features, that can be String or categorical?
I want statistics to select the characteristics that have the greatest relationship to the output variable.
Thanks to this article, I learned that the scikit-learn library proposes the SelectKBest class that can be used with a s... |
H: How to encode a column containing both string and numbers
I have a column in my dataset which contains both number and strings as the value. I want to encode the string variable so to use it for predicting. What is the best way to do this?
AI: Encoding is a way to transform categories to numerical variables, there ... |
H: Relation between hyperparamters and training set for an object detection model
I have 2 instances of an object detection model. The only difference between these two models is the training data used:
The first model was trained with a small training set
The second model was trained on a larger training set than th... |
H: Export pandas dataframe to a nested dictionary from multiple columns
Its a similar question to
Export pandas to dictionary by combining multiple row values
But in this case I want something different.
from pandas import DataFrame
df = DataFrame([
['A', 123, 1],
['B', 345, 5],
['C... |
H: How to drop points from a data series for presentation?
We monitor long running industrial engines and we have data series that we want to present on a line chart on a web page. For instance, we have sensors that monitor the oil temperature and pressure on the engine.
There are several other similar data series on ... |
H: Extracting encoded features after CatBoost
I have a dataset containing numerical as well as categorical variables.
After I've fit my dataset to a CatBoostClassifier, I want to extract the entire feature set, with the categorical variables encoded in whatever method the classifier decided to encode them.
How can I... |
H: Using a JSON dataset for lstm
I am a bit new to python. I have a json dataset which I have to use in a lstm using keras. My implementation is about emotion analysis for a set of reviews using lstm.
I have run the code below:
a = "ive probably mentioned this before but i really do feel proud of myself for actually k... |
H: What does online learning mean in Topic modeling (LDA) - Gensim
I came across this line in the Gensim Documentation- Gensim LDA - "The model can also be updated with new documents for online training."
So my assumption on what it means is - 'Once we have a model trained on one corpus, we can add new data and cont... |
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