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H: Top N accuracy for an imbalanced multiclass classification problem
I have a multi-class classification problem that is imbalanced. The task is about animal classification.
Since it's imbalanced, I am using macro-F1 metric and the current result that I have is: 51.59.
The issue that I am facing is that, this task wi... |
H: Combining sklearn pipelines with different output shape
As part of a data preprocessing step, I'm trying to create a "master pipeline" from two separate pipelines, one for numerical features and one for datetime features. The numerical pipeline removes outlier rows based on an IQR filter, whereas the datetime pipel... |
H: Parallel Data preprocessing
I am looking for a suggestion. Is it possible to implement the data preprocessing steps like missing value imputation, outlier detection, normalization, label encoding in parallel? Can I implement cuda/openmp/mpi programming for data preprocessing?
Thank you.
AI: Yes - there are a lot of... |
H: Multi head self attention output size for batches with different sequence length
I have a question regarding the self attention layer of transformers. When dealing with sequences of varying lengths in a mini-batch, we pad sequences so that all sequences in the batch have the same length.
Let's say that that most se... |
H: How to create custom action space in openai.gym
I am trying to upgrade code for custom environment written in gym==0.18.0 to latest version of gym. My current action space and observation space are defined as
self.observation_space = np.ndarray(shape=(24,))
self.action_space = [0, 1]
I understand that in the new ve... |
H: How do I find out which metadata is affecting/influencing the classification model?
I'm working with genomics data; I have a multi-class label with a matrix of numeric values (rows are the samples). Each sample may have different metadata which are not being used for training nor testing. For example, each sample m... |
H: Is there a machine learning model that is able to take reviews as input and output a new and unique blog article from them?
I am looking for a machine learning model ideally with inference speeds of no longer than a few minutes that is able to take in n human written reviews and output a blog article from them.
The... |
H: How can I do mathematical operations to two columns of a CSV file and save the result in a new CSV file?
I have a CSV file (such as test1.csv). There are tabular values like the following.
S1 S2 S3
4.6 3.2 2.1
3.2 4.3 5.4
1.4 3.4 6.1
I want to do mathematical operations, such as R1=(S1+S2... |
H: How to calculate accuracy of an imbalanced dataset
I like to understand what is the accuracy of an imbalanced dataset.
Let's suppose we have a medical dataset and we want to predict the disease among the patients. Say, in an existing dataset 95% of patients do not have a disease, and 5% patients have disease. So cl... |
H: Perform k-means clustering over multiple columns and get the cluster center values?
I read here how to show the number of clusters over $n$ columns.
I would like to know how to get in a table, the values of the clusters centers. Could someone help me with this?
AI: sklearn.clusters.KMeans has an attribute cluster_c... |
H: Precision, recall and importance of them in the imbalance problem
I have a test dataset. The dataset is an imbalanced dataset. The total training instances for the dataset is 543 among them minority class(yes) is 75 and the majority class(No) is 468. The class of interest is minority class(yes). I used the Naive Ba... |
H: RNN for continuous, real-time learning without pre-training
I am learning ML and I'm trying to solve this problem
Create a rock paper scissors game where the AI is able to beat the player more than 50% of the time.
My initial intuition was to use an RNN with an LSTM. I imagined my AI analyzing the history of the ... |
H: Predicting a real number in a fixed range
Suppose we need to predict a real number in fixed range, for example, [0 .. 5], and our Y can be 3.14, 2.4654 etc.
What is the name of this kind of tasks (to be able to search further) and what are the approaches to solve this problem?
AI: This is a regression problem with ... |
H: Understanding MDP variants and "model-free" RL algorithms
RL is based on MDPs. But MDPs have other useful variants such as Semi MDP (variable time), POMDP (partially observable states) etc.
Some industrial problems seem to be better suited for SMDP and/or POMDPs. For example optimizing maintenance, the time between... |
H: Is R programming a good way to start with Data Science?
I am a software engineer (currently CTO) specialized on web and mobile applications picking up data science skills. I do this mainly for future projects within my startup that works in digital healthcare.
For this I have started to learn via Coursera, specific... |
H: Average number of records by ID
I have a dataframe with IDs and booking refs, looking like the simplified example below.
ID
BookingRef
001
2019/32323
002
2011/23232
002
2017/7u4922
In the above example, 001 has one booking and 002 has two bookings in total so the average number of bookings for custo... |
H: Threshold determination / prediction for cosine similarity scores
Given a query sentence, we search and find similar sentences in our corpus using transformer-based models for semantic textual similarity.
For one query sentence, we might get 200 similar sentences with scores ranging from 0.95 to 0.55.
For a secon... |
H: Online Learning/Continual Learning for tree-based Algorithms
Every example I come across any kind of iterative learning on Random Forest/XGBoost/LightGBM, it just continuously grows the number of estimators for new batches of data by n_tree/n_estimators/num_boost_rounds every time .fit() gets applied [...]. Most of... |
H: Accent on special words or characters can affect machine learning algorithms?
Good evening, I wanted to ask this question about the accent in words or special characters can affect machine learning algorithms. I'm looking to do a job. I would like to know a recommendation for an article or book that I can use as a ... |
H: Why use cosine similarity instead of scaling the vectors when calculating the similarity of vectors?
I'm watching a NLP video on Coursera. It's discussing how to calculate the similarity of two vectors. First it discusses calculating the Euclidean distance, then it discusses the cosine similarity. It says that cosi... |
H: How to Combat Data Drift
I have customer demographic data that include columns like: age, the first half of the postcode, occupation (there is a defined list of possible occupations), and more. Each month I get a new batch of 1000 rows of this type of data (which is not labelled) and I need to put this into my trai... |
H: How to stop my CNN getting confused between 3s and 8s, and 1s and 7s?
I am trying to train a CNN, using the MNIST dataset (which I perform data augmentation on), to classify numbers on a sudoku grid from 0-9.
While mostly successful, my network seems to get confused between 3s and 8s, and 1s and 7s because of how s... |
H: Detrend a time series
I am fairly new to forecasting and I am trying to create a demand forecast for my organization; I am following the methodology outlined here. In step 12 of the process, the author subtracts the trend value from observed value to detrend the data (code shown below):
# Using statmodels: Subtract... |
H: What is the difference between Data Modeling and Data Processing?
When discussing big data, it is sometimes mentioned that data modeling can be done by using a tool like map reduce, while data processing may be performed by apache spark. What is the difference between data modeling tasks, and data processing tasks?... |
H: Is Pearson correlation a good loss function?
I want to do a data science project. I want to use price history to predict future prices.
I want to use correlation(y, y_pred) as my loss function but I found it's hard to calculate first deter, and second deter.
Has anyone used correlation as loss function, and is it g... |
H: Model from an aggregate
I’m in a place where we’re unable to train models on data due to GDPR. What I want is to predict people getting a job (y) given (x,x,x,x…) their employment type working full time or part time, work sector, area and age.
Since I cannot train on real data, I created an aggregate from the real ... |
H: Inference Process in Autoregressive Transformer Architecture
I'm abit confused about how the inference/prediction process works in transformer.
For simplicity suppose it is a transformer for translation. My understanding is that when training, the whole input and output sentences are put into model. This is possibl... |
H: Improve CNN classification accuracy
I am training a CNN model with about 20.000 images with two classes each 10.000 images. The size of the images vary between 50*50 pixel and 1000x500 pixels. I am resizing all images to the average size of all images, which is 350x150 pixels. Then training a CNN with this architec... |
H: Where is the VGG in Cartoongan?
When I read through the paper of Cartoongan [CartoonGAN: Generative Adversarial Networks for Photo Cartoonization], I was so confused about where is vgg located in the entire network.
Based on the paper about GAN [Generative Adversarial Nets], I could not see anywhere that vgg exists... |
H: Can we visualize a feature space with 4 or more dimensions?
I have a dataset with 6 independent variables, I have trained this dataset on a Support Vector Machine. Is there is a way in python or R to visualize the feature space with the hyperplane? Or for example, can I plot many 2D plots such that every plot has t... |
H: How to calculate a trend to use as a feature in a machine learning model?
In a machine learning model one of the features is unemployment:
Month Unemployment
May-2022 3.6%
Jun-2022 3.7%
Jul-2022 3.8%
Aug-2022 3.9%
What I need is to use as an additional feature the trend in the last thre... |
H: I am struggling to understand the point of supervised ML models in real world scenarios
Sorry for maybe a stupid question, but I can't seem to find any explanation of it online. If supervised machine learning only works on labeled datasets - you can't use it to predict a value of unlabelled data after the model is ... |
H: Inverted ROC curve
I am using tidymodels package in R. Running random forest to classify three classes. There are about 8000 samples in total and 130 features. This is how the ROC curves look like.
The predictions for all 3 classes are actually pretty good and fit with expectations. The misclassified samples are a... |
H: How can I say if I have a class imbalance issue in my data?
Assuming I have this dataset:
Label --- %Total
0 -------- 18.53%
1 -------- 8.18%
2 -------- 26.22%
3 -------- 16.46%
4 -------- 8.62%
5 -------- 9.58%
6 -------- 5.88%
7 -------- 6.53%
I could say I have a class imbalance problem ?
Is it mandatory in this... |
H: SkLearn Categorical Naive Bayes Vs Mathematical theory of Naive Bayes
The Naive Bayes classification based on the following formula
$P(C_i|X) = {P(X|C_i)P(C_i) \over P(X)} ... i)$
$P(X|C_i)$ is the posterior probability of $X$ conditioned on $C_i$, $P(X)$ prior probability of $X$, $C_i$ represents the class.
Now if... |
H: One word changes everything NLP
I have a classification model (BERT) that classifies sentences as either question or normal sentences. But whenever a sentence has "how" word, the model chooses "question" class.
How can I solve this issue? (I have a very big dataset.)
AI: Very likely, the majority of the sentences w... |
H: Stacking: Use predictions of train or test to create features for level 1 classifier
The question is pretty simple.
In stacking, the predictions of level 0 models are being used as features to train a level 1 model.
However, the predictions of what data? Intuitively it makes more sense to predict the test set and u... |
H: clustering time series with different sized time series
I have read this article on towardsdatascience and they teach how to cluster time series using the DTW distance and the TimeSeriesKMeans from the tslearn.clustering library.
I also read the official documentation and I found a note.
Notes
If metric is set to ... |
H: Confusion regarding One Hot Encoding
We use one hot encoding with nominal variables so that we don't impose any order relationship. If we have a categorical variable that has three categories: Red, Blue, Green. The one hot encoding of this variable will be : Blue --> [1,0,0], Green --> [0,1,0] and Red --> [0,0,1]... |
H: How does one perform a Canova-Hansen test in Python?
I am referring to the documentation here, but it does not give many examples on how to actually perform the test. I have a pandas dataframe with two columns:
Column 1 is first day of every week,
Column 2 is demand, and this data goes back over 150 weeks.
How wo... |
H: Is there a way to map words to their synonyms in tfidf?
I have the following code:
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans
import pandas as pd
sentences = ["I have the ability", "I have the weakness", "I have the capability", "I have the power"]
tfidf = Tfidf... |
H: when I run Random Forest classification model then at every rows of my train data set show this error (ValueError: could not convert string to float:)
'''
from sklearn.ensemble import RandomForestClassifier
forest = RandomForestClassifier(n_estimators = 500, max_depth = None, min_samples_split=2, min_samples_leaf =... |
H: Why is max_features ordered by term frequency instead of inverse document frequency
In the docs: https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html
it is explained that max_features is ordered by term frequency across the corpus. Why not use the idf?
AI: The reaso... |
H: working principle of Support Vector Machine
I have a dataset consisting of numerical features and categorical features. I want train the training set using SVM. SVM is a quadratic optimization algorithm. I would like to know the how SVM works on categorical data. Can anyone share any references, links to research p... |
H: Increasing/Decreasing importance of feature/thing in ML/DL
I have 3 cases:
I have a classification model that will be used to classify cats and dogs. On my train data dog pictures has a watermark on them, but cat pictures don't. The problem is: Whenever I have a watermark on a cat picture, the model will predict t... |
H: How do the intercept and slope calculated in linear regression relate to the output of lm?
I have been looking at how to calculate coefficients by hand
and the example produces
$Y = 1,383.471380 + 10.62219546 * X$
However the output shown of lm does not show these values anywhere.
How do I reconcile the results of ... |
H: Predict actual result after model trained with MinMaxScaler LinearRegression
I was doing the modeling on the House Pricing dataset. My target is to get the mse result and predict with the input variable
I have done the modeling, I'm doing the modeling with scaling the data using MinMaxSclaer(), and the model is tra... |
H: what is the difference between 'object detection' and 'outlier detection' in computer vision?
If I'm looking at drone footage, and I'm looking for tennis courts, then I'm doing object detection.
If I decide that a tennis court is an 'outlier' as opposed to the rest of the landscape, am I now doing 'outlier detectio... |
H: Machine learning algorithms for tabular dataset
I have a dataset with 120 features and 5000 instances. The dataset is combination of categorical and numerical values. It is a tabular dataset. My problem is a binary classification problem. I trained my dataset with all classic classification algorithms like Naive Ba... |
H: Should I annotate additional information besides the categories I already need in a text?
I have a dataset with bank transfer reasons. They vary a lot because humans wrote them.
From the reasons that are linked to invoice payments I need to extract several things:
invoice number(s)
IBAN
counterparty
Before I use ... |
H: How do I know that my weights optimizer have found the best weights?
I am new to deep learning and my understanding of how optimizers work might be slightly off. Also, sorry for a third-grader quality of images.
For example if we have simple task our loss to weight function might look like this:
As far as I unders... |
H: Testing the impact of events on time series
Context
I am working with product data for a retail company. I have the daily impressions (number of times it was viewed online) for all products over a 30 day period (can get more data).
Here is the data for one product:
on_sale = [False, False, False, False, False, Fals... |
H: Feature scaling in Linear Regression
I always use Linearregression() class in sklearn library for creating a linear regression model. According to my understanding, we need feature scaling in linear regression when we use Stochastic gradient descent as a solver algorithm, as feature scaling will help in finding the... |
H: Filter out transactions occurring within a timeframe with the same amount
I need to apply some filtering on a data frame using pandas.
Basically my data frame has the following column:
ID - The row id of the transaction
Timestamp - object was transformed to datetime format
Amount - float
The data frame consists o... |
H: Transform dataset to regression problem by sorting?
I have a raw unlabeled dataset, and I want to design a model to perform a regression. In my dataset, it does not make sense to give each observation a value, but it does make sense to sort them. Can I implement an algorithm to create values for each observation by... |
H: Combine datasets of different domains to ehance generalizibility
so I try to implement an Emotion Classifier, which should detect several emotions from a text. There are several datasets for this (ISear, GoEmotions, etc.). However, a lot of them come from different domains, e.g. from Chats, Blogs, Newsarticles, etc... |
H: Logistic Regression using Logisticregression() class
In the documentation of Logisticregression() offered by sklearn library, it states the following note:
The underlying C implementation uses a random number generator to
select features when fitting the model. It is thus not uncommon, to
have slightly different ... |
H: Why is it an advantage "that Markov chains are never needed" to obtain gradients?
In the original GAN (Generative Adversarial Network) paper, Generative adversarial networks by I. Goodfellow, J. Pouget-Abadie, M. Mirza et. al. they state an advantage of the GAN is "that Markov chains are never needed, only backprop... |
H: How many matrix combinations is this?
Input Matrix:
A B C D E
A 1 - - - -
B - 1 - - -
C - - 1 - -
D - - - 1 -
E - - - - 1
"-": arbritary values
"1": locked "1" value (diagonally locked, ref below)
Output Matrix (one possible example after some arbitrary optimization based on the arbitrary values in the matrix)... |
H: Time series data for Prophet model
I have a time series data that has some missing dates. For example, in the dataset below '2017-08-06' is missing.
To enter this data in the Prophet model, do I have to create the data frame with all the missing dates? I know the Prophet model does not require the missing data for... |
H: Building a Time Series Model
I am working on building a time series model, but the dataset I have only has date features for the year; the month and date are not available. What would be a suitable model to use and is it even possible? The dataset is an Excel file from different years I have merged into a single sh... |
H: LSTM Feature engineering: using different Knowledge Graph data types
For a research project, I'm planning to use an LSTM to learn from sequences of KG entities. However, I have little experience using LSTMs or RNNs in general. During planning, a few questions concerning feature engineering have come up.
Let me give... |
H: Time series data transformation for Prophet model
I have a time series data that looks as follows
The data has the following frequency distribution
Here is the Q-Q plot
It looks like the data is exponentially distributed. My assumption is that the noise or error component of the data will also be similarly distr... |
H: Accuracy is getting worse after text pre processing
I'm working a multi-class text classification project.
After splitting the dataset into train and test datasets, I've applied the below function on the train dataset (AKA pre processing):
STOPWORDS = set(stopwords.words('english'))
def clean_text(text):
# ... |
H: How does the universal approximation theorem connect to classification problems?
Feedforward Neural Networks are used a lot for classification problems and they seem to do a good job. How can this be justified?
As the universal approximation theorem for neural networks assumes there is continuity, which clearly is ... |
H: How to assess predictive power of each predictor in case of linear regression
For a predictive model with binary target, we can assess the predictive power of each predictor by calculating their information value. What is the equivalent of IV when the target variable is continuous? Should I look at $R^2$ value of i... |
H: N-gram language model for preposition predition
I am trying to build N gram models to predict the missing prepositions of a text corpus.
I would want to have some guidance on if I'm understanding and doing things correctly.
So the N gram model is basically just a collection of posterior probabilities? Pr(this word ... |
H: How to calculate the significance of each feature?
I built a predictive model using an elastic net regression model with sklearn. The model R2 = 0.015. I know SHAP method could provide the importance of the features. However, How to calculate the significance of each feature? (Get which feature is significant or wh... |
H: Finding points in 3D space - SVM
In the SVM classification, we use planes to classify the labels points if the dataset has 3 input features. We need to use planes when input features are 3. I am describing a toy dataset with 3 input features as follows
Study_time rest_time pass_time label
40 10 ... |
H: Official page of Weka for SVM java code
I am using Weka to train a model from few days. I know Weka use Java code to implement a classifier. I also heard that Weka has some github pages to describe the java code for the classifiers. I like to know the SVM java code which is used in WEKA. I found few webpages descri... |
H: Mathematically rigorous NLP
I'm looking for resources (books/articles/whatever) that provide mathematical formalization of NLP and statistical language theory. By that I mean clear exposition of the subject in terms of probability spaces (measure spaces) and so on. For example, many NLP books (like the Manning's on... |
H: "Chi-squared approximation may be incorrect" on hypothesis test
I'm doing a hypothesis test to determine if the data follows binomial distribution with p=0.31, and I'm getting a warning when running this:
chisq.test(x = c(36,48,38,23,10,3),p = dbinom(0:5,5,0.31))
Chi-squared test for given probabilities
data:... |
H: How to use new data with Principal Component Analysis (PCA)
I reduce data with PCA already from 9 to 3 feature. If I have real data new row which I want to use with pre-train model (.h5). Can I change data 9 feature to PCA 3 feature only one row for test with model ?
import numpy
from pandas import read_csv
from sk... |
H: Does combining real data with simulation data improve the performance of machine learning?
I am trying to train a machine learning model to help me classify some real data. Since the acquisition and labeling of real data can be very expensive, the training data is generated with simulation. However, the trained mod... |
H: Convert English active voice sentences into passive voice sentences using Machine learning
Is there any machine learning algorithm developed to convert an English active voice sentence into a passive voice sentence? And what are the datasets available related to that purpose? And also if there are available source ... |
H: Abstracted text summarisation and generation from weighted keywords
Suppose I have a list of weighted keywords/phrases, such as "solar panel", "rooftop", etc. The weights are in [0,1] with higher weights indicating a stronger preference for specific keywords, so "solar panel" may have a weighting of 0.3 and "roofto... |
H: Encoding for Linear Regression
I have a CSV file with salary information and other columns.
I am trying to transform some of these columns into proper values, for a LinearRegression and a SGDRegressor, or some other. Because, I don't think that the LinearRegression in sklearn can handle the data bits as is.
Data:
... |
H: Tensorflow - do I need to learn computer vision before linear (timeseries) regression?
I'm a newbie to tensorflow / keras and I am currently working my way through Deep Learning with Python (2nd edition) by Francois Chollet.
I understand the basics of Computer vision and the MNIST examples but I'm not really intere... |
H: Select the best feature selection method for classification
I am trying to make predictions (using Weka) on a tabular dataset. It is a categorical dataset which is encoded by label encoder.
I got a good result for SVM and Logistic Regression, namely the accuracy is around 85%.
The dataset is high-dimensional and I... |
H: Good NLP model for computationally cheap predictions that can reasonably approximate language model given large training data set
I have a corpus of about one billion sentences, in which I am attempting to resolve NER conflicts (when two terms overlap in a sentence). My initial plan is to have an SME label the corr... |
H: Understanding the tensorboard plots on a stable-baseline3's PPO
So there are various plots that are provided when training a stable-baselines3's PPO model, so I thought you'd help me fill up the gaps with what is not quite clear to me:
rollout/ep_len_mean: that would be the mean episode's length. What is the expec... |
H: Fine tuning BERT without pre-training it on domain specific corpus
I'm building an internal semantic search engine using BERT/SBERT + ElasticSearch 8 where answers are retrieved based on their cosine similarity with a query.
The documents to be searched are somewhat domain-specific, off the top of my head estimati... |
H: What is the best way to handle missing observations for ACF / PACF?
I have a time series with daily retail sales with two types of missing values
Stores are closed on Sundays such that there are no observations.
Stores are closed on public holidays (approx. 10 days per year)
I want to calculate and plot ACF and P... |
H: CNN model why is ReLu used in Conv1D layer and in the first Dense Layer?
I have a problem. I have a CNN model which is used for an NLP problem. This is written in Python. I have questions about this, which I can't find an answer to.
Why is ReLu used inside the Conv1D layer and not Softmax ?
Why is ReLu used again ... |
H: ValueError: Found unknown categories ['IR', 'HN', 'MT', 'PH', 'NZ', 'CZ', 'MD'] in column 3 during transform
I am trying to use Linear Regression, to predict salary in USD. I have the following data:
Data:
607 records
Numerical columns: year, salary, salary in USD
Categorical columns: experience, type, residence,... |
H: Grid-search for a multi-output regression task using Scikit-learn's API
I'm trying to make a model for a multi-output regression task where $y=(y_1, y_2,..., y_n)$ is a vector rather than a single scalar. I am using Scikit-learn's MultiOutputRegressor method to train and make a model for each $y_i \in y$ separately... |
H: How does BERT produce CLS token? Internally does it do max-pooling or avarage pooling?
I ran experiment to compare max-pooled word tokens vs CLS token for sentence classification and CLS clearly wins. Trying to understand how BERT generates CLS token embedding if its better than max or avg pooling.
AI: The output a... |
H: Why compare multiple machine learning algorithms and then decide which algorithm to use for fine tuning?
I have a problem.
There is a dataset A, which deals with a classification problem. And for this dataset, several different baseline algorithms have been defined and computed.
In addition, three models were used:... |
H: Pytorch Neural Network that tries to approximate $z_i = x_i^2 + y_i^2$ not converging to solution
Background
I am teaching myself Pytorch, as a Mechanical engineering technology (MET) faculty. My end goal is to replace many data-driven heat transfer and Fluid dynamics models with Neural network approximations. Thi... |
H: Why is my model overfitting?
I am building a classification model based on some machine performance data. Unfortunately for me, it seems to over-fit no matter what I change. The dataset is quite large so I'll share the final feature importance & cross validation scores after feature selection.
#preparing the data ... |
H: Is it possible to tell if one activation function is better than the other one based on their graphs?
I am attempting to formulate my own activation function. However, I'm new to neural networks, am not yet ready to test it, but would want to know if I already landed on a better activation function than my benchmar... |
H: Why an already trained model is not generalizable to another related dataset?
A model is trained to predict the median temperature of Boston. The resulting model works well according to their validation data. However, this model performs poorly when used to predict the temperature of Washington. Explain the reason ... |
H: How is loss calculated in truncated BPTT, for a many to one problem?
In many resources I refered to such as Justin Johnson's Lecture 12 on RNN, truncated BPTT is explained as the process of feedforward and backpropagate for smaller chunks of the sequence. These explanations says loss is calculated for each chunk an... |
H: What makes an ROC curve a curve and why do the values change?
I have a problem. I am currently looking at a classifier and I would like to examine this using an ROC curve as a metric. However, questions have arisen to which I can not find an answer.
A ROC curve describes the following
ROC curves are frequently use... |
H: Why is it useful to use different word splitting with different tokenizers?
I have a problem. I have a NLP classification problem.
There are different methods to decompose sentences into tokens, for example in whole words or in characters. Then there are different tokenizers like:
TF-IDF
Binary
Frequency
Count
My... |
H: Why so discrepancy between ARIMA and LSTM in time series forecasting?
I have this time series below, that I divided into train, val and test:
Basically, I trained an ARIMA and an LSTM on those data, and results are completely different, in terms of prediction:
ARIMA:
LSTM:
Now, maybe I am passing, in some way, t... |
H: Does it help to have similar values for features in train and test data to make accurate predictions?
I am quite new to some concepts of machine learning and having hard time understanding the following.
Suppose I have a supervised classifier (random forest) trained with a dataset with several features.
Do the feat... |
H: Append Existing Columns to another Column in Pandas Dataframe
I have a data that looks like this:
The T2M indicates the temperature, and the followed row is the year. I want to append all the similar parameters columns under a single column having all the years, I will end up with one T2M column only, and the fina... |
H: ignore exponent of a word in dataframe
I have a dataframe where there are columns which have exponent in their values(strings/words).
Example
Pandas reads them as Rasha, Fatiguec,Pyrexiab. is there any way i can make it read properly. or even some other way to remove the exponent in those words
UPDATE: I found th... |
H: What model to fit to call center data
I have a dataset with calls from day 1 to day 340. What model can I fit to mathematically capture the pattern?
There are only 1 or 2 digit number of calls on all days except day 61.62.63 and 121.122.123 and 170 days when there are 3-4 digit number of calls
AI: I don't think you... |
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