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H: Which between random forest or extra tree is best in a unbalance dataset?
I have an unbalanced dataset, with 3 classes, with 60% of class 1, 38% of class 2, and 2% of class 3.
I don't want to generate more examples of class 3, and I cannot get more examples of class 3.
The problem is that I need to choose between R... |
H: Choosing attributes for k-means clustering
The k-means clustering tries to minimize the within-cluster scatter and maximizing the distances between clusters. It does so on all attributes.
I am learning about this method on several datasets. To illustrate, in one the datasets countries are compared based on attribut... |
H: Why do we use 2D kernel for RGB data?
I have recently started kearning CNN and I coukdnt understand that why are we using a 2D kernel like of shape (3x3) for a RGB data in place of a 3D kernel like of shape (3x3x3)?
Are we sharing the same kernel among all the channels because the data would look the same in all th... |
H: K-means and LDA for text classification
I hope to explain in a clear way what I would like to do. I have more than 50000 tweets and I would like to add some labels on topics. So I have used LDA for doing this. I have also used k-means to group them and try to predict the cluster (but not the topic). I would like to... |
H: In model validation (log regression), can AUC for the test sample be higher than that for the train sample?
I have a relatively simple model (with 8'000 cases, 5 predictor variables) predicting a dihotomised outcome. It has an AUC of 0.82 (95%CI 0.82-0.83). Prediction in temporal (N=240) and external validation (N=... |
H: Is this attribute numeric or categorical (ordinal)? Help!
So I have this dataset I need to perform several techniques on as part of a data mining/machine learning project of some sort in PYTHON. There are a couple of features however, that have me very worried as I don't know whether I whould handle them as Categor... |
H: Multidimensional K-Means wiith sklearn, centroids problem when plotting
I am working with a dataset (X) to predict 12 clusters with K-Means using python SKLEARN library:
numClusters= 12
kmeans = KMeans(n_clusters=numClusters).fit(X)
centroids = kmeans.cluster_centers_
# Predicting the clusters
labels = kmeans.pred... |
H: k-means and LDA for text classification: how to test accuracy?
I have many tweets that I would like classify based on their similarity.
Unfortunately I am not quite familiar with text-classification and nlp, so I had to read a lot of documents before having an idea on the topic.
My tweets have no labels so I cannot... |
H: Why PyTorch is faster than sklearn models?
Recently, I get to know about the hummingbird library for Python. I trained a RandomForest on a 10M-sized dataset with 2 labels. With sklearn it was taking 450 ms for inference. But after converting the same model to PyTorch, now it takes 128ms on CPU inference.
If both ar... |
H: Best Approach to Forecasting Numerical Value Based on time series and categorical data?
Consider a dataset of thousands of car repairs that have been performed. In simplest of terms, the columns to consider are the time of year when it was broken (seasonal changes in demand for car repairs), type of damage to car (... |
H: Association Rules with Python (coded dataset)
I have this dataset which I really need to use association rules techniques on. The dataset has like 90 variables, many of which are ordinal. Thing is, the data is already coded using numbers instead of strings (e.g. bread = 4 instead of "bread") as well as some re-scal... |
H: Why don't we transpose $\delta^{l+1}$ in back propagation?
Using this neural network as an example:
The weight matrices are then
$$ W_0=[2\times4], W_1=[4\times4], W_2=[4\times2]$$
To find the error for the last layer, we use
$$ \delta^{[2]} = \nabla C \odot \sigma'(z^{[2]})$$
which makes sense. This will produce ... |
H: Text Classification : Classifying N classes vs rest of the classes
Apologies if this is naive, I am fairly new to the domain.
I have a requirement where I am trying to classify 2 types of text data, i.e, I have got 2 classes to classify my data upon. I am able to get acceptable results for them using word vectors, ... |
H: Understanding output probabilites of xgboost in multiclass problems
I would like to understand the output probabilities of a xgboost classifier (or any other decision tree ensemble based classifier) in the case of a multiclass problem.
For example: We have 5 different classes and a trained model on some data belong... |
H: Final Model fitting - subset vs entire training data
If I used a subset of the entire available training data for model tuning and hyperparamater selection, should I fit the final model to the subset training dataset or the entire available training data?
For example, if I have 1M samples available and I took a 100... |
H: Linear discriminant analysis in R: how to choose the most suitable model?
The data set vaso in the robustbase library summarizes the vasoconstriction (or not) of subjects’ fingers along with their breathing volumes and rates.
> head(vaso)
Volume Rate Y
1 3.70 0.825 1
2 3.50 1.090 1
3 1.25 2.500 1
4 0.75 1... |
H: Hive / Impala best practice code structuring
Coming from a DWH-background I am used to putting subqueries almost everywhere in my queries. On a Hadoop project (with Hive version 1.1.0 on Cloudera), I noticed we can forego subqueries in some cases.
It made me wonder if there are similar SQL-dialect specific differen... |
H: Rolling average: when is it possible to consider it?
I would like to know if I can consider rolling average to predict the future trend of sells.
I collected data from January 2020 to March 2020, day by day, on sells in a shop and I would like to run some analysis.
I was considering rolling average on multiple peri... |
H: Loaded model predicts well in colab but gives same label and accuracy when downloaded
I have developed a Recurrent Neural Network to perform sentiment analysis on tweets using the Kazanova/sentiment140 dataset in Kaggle.
The model looks like this:
def scheduler(epoch):
if epoch < 10:
return 0.001
else:
... |
H: Linear Regression with vs without polynomial features
I have a conceptual question about why (processing power/storage aside) would you ever just use a regular linear regression without adding polynomial features? It seems like adding polynomial features (without overfitting) would always produce better results. I ... |
H: The OLAP (On-Line Analytical Processing) cube with 4 dimensions
A typical OLAP cube looks like this:
As I can see, this cube can work with 2 or 3 dimensions, but what if I have 4 dimensions to produce facts?
Should I use star schema instead when having more than 3 dims?
AI: Your example is a star schema, it's just... |
H: Why my training and testing set are about 99% but my single prediction does wrong prediction?
I have performed fruits classification using CNN but i am paused at a point where all things are going right confusion matrix accuracy score all are correct it seems there is no overfitting but it always classifies wrong f... |
H: Should the weights for CrossEntropyLoss be exactly the inverse of the propotions of training data?
I have a classifier network which chooses one of three classifications, and uses cross entropy loss as the loss function. If the proportions of training data are 100:10:5 for each classification, should I automatical... |
H: Is rolling of biased dice random phenomenon?
Random phenomenon is a situation in which we dont know what the out come is going to come.
Rolling of unbiased dice is a random phenomenon since we dont know what number is going to come. We can only say every number has
1/6 probability.
Rolling a biased coin on other ha... |
H: Compare similarities between two data frames using more than one column in each data frame
This work started by comparing two columns in each data set in pandas.
Previous research:here
A lot of results online show how to compare 2 data frames with 1 column
I'm trying to learn how to compare and extract similaritie... |
H: Multiple models in the same notebook
Having working on data sets, sometimes we want to keep track of mtiple models with different architectures which work on the same data set on which we have made some transformations and preprecessing of data has been done.
So I would like to know what is the elegant way to work ... |
H: What is a good interpretation of this 'learning curve' plot?
I read about the validation_curve and how interpret it to know if there are over-fitting or underfitting, but how can interpret the plot when the data is the error like this:
The X-axis is "Nº of examples of training"
Redline is train error
Green line i... |
H: Separating styles of numbers for simple digit classification
I am just getting started with my first simple digit classifier, so my doubts are at a pretty low level. In every dataset of digit images I've seen so far, different variants of each digit are grouped together, for example:
All of these images represen... |
H: what is the meaning of $\mathbb{R}^{768\times (768 * 2)}$?
Hi I'm an undergraduate student interested in Machine Learning. I was reading a paper from ICLR 2020 and came a cross a weird looking vector dimensions.
Can anyone tell me what this means??
$\mathbb{R}^{768\times (768 * 2)}$
Does this mean that in python nu... |
H: Is this overfitting?
I read about the validations curves, and the following plot is similar to overfit, but in this case, the validation curve doesn't' growth again.
So is this overfit? why?
Thanks
AI: So, overfitting occurs when the model is complex enough to fit very well with examples observed in the training da... |
H: is it possible get a overfit underfit comparation between models, with this chart? (homework)
I am trying to interpret this chart.
I am not sure how to interpret this, because, I think that the fact of the for examples LGBM Validation error, is wide and similar to train boxplot, there arent problem of overfitting, ... |
H: 3D visualisation and post analysis tool
I am looking for a 3D plotting and post analysis tool, specifically which can generate figures like the following. It is preferred the tool has a Linux distribution. The figure is copied from here.
AI: If anyone is interested, I found that such graphs can be created with the ... |
H: How similar is Adam optimization and Gradient clipping?
According to the Adam optimization update rule:
$$m \leftarrow \beta_1 m + (1 - \beta_1)\nabla J(\theta)$$
$$v \leftarrow \beta_2 v + (1 - \beta_2)(\nabla J(\theta) \odot \nabla J(\theta))$$
$$\theta \leftarrow \theta - \alpha \frac{m}{\sqrt{v}}$$
From the equ... |
H: Expectation Maximization Algorithm (EM) for Gaussian Mixture Models (GMMs)
I'm trying to apply the Expectation Maximization algorithm (EM) to a Gaussian Mixture Model (GMM) using Python and NumPy. The PDF document I am basing my implementation on can be found here.
Below are the equations:
$\mathrm{E}-\text{step:}$... |
H: Decision tree and SVM for text classification - theory
I used 4 classifiers for my text data: NB, kNN, DT and SVM. As for NB and kNN I fully understand how they work with text - how we can count probabilities for all words in NB and how to use similarity metrics with TF-IDF vectors in kNN I don't understand at all ... |
H: what's this approach to spatiotemporal data named as?
I have some sequential data (e.g. audio, video, text etc.) and I am using this approach to classify sequences. I am sure there's a name for it, but I can't think of it:
vectors =
t1,[v1_0....v1_n]
t2,[v2_0....v2_n]
:
:
tm,[vm_0....vm_... |
H: Dealing with missing data
I have a question about data cleaning. I am a novice and have just started learning in this field so please pardon my ignorance. Suppose there are two columns and based on some samples taken from both the columns you find the correlation coefficient to be high. Now for the values that aren... |
H: Why do we add "αd" to N in Laplace Smoothing?
I just started to learn Naive Bayes algorithm. Then I learned to use Laplace smoothing to avoid getting probability of zero.
I understand the purpose of using it, but, in the expression of Laplace smoothing below, I do not really understand why we need to add "αd" to N ... |
H: Data visualization with extreme far away points
I want to show points across two groups. However, for both groups, there are some points which are far away from most of the other points within each group, shown below. Any suggestions for this situation? Thank you.
AI: If you want to see the distribution of the data... |
H: Confusion regarding confusion matrix
I am confused on how to represent the confusion matrix -- where to put the FP and FN. Link1 and Link2 show different confusion matrix for binary classification. The rows represent the actual and columns represent the predicted values. Based on my understanding, the correct confu... |
H: Stacking and Ensembling methods in Data Science
I understand that using stacking and ensembling has become popular, and these methods can give better results than using a single algorithm.
My question is: What are the reasons, statistical or otherwise, behind the improvement in results?
I also understand that at a ... |
H: How to determine the correct target for classification probability when the observed samples are probabilities of each class?
I have data in which each event's outcome can be described by a probability of a categorical occurrence. For example, if all of the possible class outcomes are A, B, C, or D suppose in one e... |
H: What are the merges and vocab files used for in BERT-based models?
The title says it all. I see plenty online about how to initialize RoBERTa with a merges and vocab file, but what is the point of these files? What exactly are they used for?
AI: The vocab file contains a mapping from vocabulary strings and indices ... |
H: The impact of using different scaling strategy with Clustering
I'm currently learning about clustering. To practice clustering, I am using this dataset.
After running K-means clustering for multiple values of k and plotting the results, I can see that scaling is affecting the results (within-cluster SSE) and I want... |
H: Predicting next element of a sequence given small amount of data
I have data of bank branches and amount of revenue they have generated in a month. The data looks like this:
I am tasked to find the expected revenue for the branch for the next month using machine learning. Initially I was planning to use LSTM netwo... |
H: I am trying to figure out the stationarity of time series data
Here, this plot shows the number of customers served per day from 1 jan 2018 to 31 dec 2019.
I grouped the entire data by each month and calculated the average and variance per month.
This is the average
This is the variance
I also ran an augmented d... |
H: Build a sentiment model from scratch
I would like to know how I can create a sentiment model from scratch.
I have my data, list of texts, with no labels about sentiment.
Author Quotes
Dan Brown “Everything is possible. The impossible just takes longer.”
Dan Brown “Great minds are always feared by lesser min... |
H: How to use unigram and bigram as an feature on SVM or logistic regression
How to use unigram and bigram as an feature to build an Natural Language Inference model on SVM or logistic regression?on my dataset i have premise, hypotesis and label column. I'm planning to use the unigram and bigram of the premis or hipot... |
H: Lower loss always better for Probabilistic loss functions?
I am working on an neural net int Tensorflow that predicts percentages for win, draw, loss for given data of a game. The labels I provide are always {1, 0, 0}, {0, 1, 0} or {0, 0, 1}. After some epochs my accuracy doesn't increase any further, but the loss ... |
H: Creating a new Dataframe with specific row numbers from another
I've found other posts that refer to creating a new dataframe using specific conditions from another (like ID = 27, etc.) but nothing that allows me to make a new dataframe from specific row numbers of another.
Here is what I have tried so far:
To add ... |
H: Permutation test on two groups
I am trying to use a permutation test to test my hypothesis.
I want to make sure I am understanding concept of permutation correctly.
I have control and experimental group. Then I combine them and resample from combined dataset randomly calculating desired statistics. Do this N time a... |
H: How does Pytorch deal with non-differentiable activation functions during backprop?
I've read many posts on how Pytorch deal with non-differentiability in the network due to non-differentiable (or almost everywhere differentiable - doesn't make it that much better) activation functions during backprop. However I w... |
H: Scikit-learn: train/test split to include have same representation of two different types of values in a column
I have a dataset of online purchase orders that contains two types of customers:
Customers who have an account and thus are known customers with unique customer number.
Customers who do not have an accou... |
H: Decision Tree : how to determine target in a model with no labels?
I am studying classification algorithms using decision tree approach in Python.
I would have some questions on this topic, specifically regarding the target (y) in my dataset.
I have a dateset made by 20000 observations and a few fields:
Customer
r... |
H: Stacked Model performance?
I am currently working with a dataset that seems very easily separable and I have an accuracy of 99% for SVM (NN-98%, RF-98%, DT-96-97% and I have checked for leakage & overfitting). As part of my project, I am also learning how to implement a hybrid model but its accuracy is also 99% (1 ... |
H: How to create ROC - AUC curves for multi class text classification problem in Python
I am working on a multiclass text classification problem and trying to plot ROC Curve but no success so far. Tried many solutions available but didn't work. Kindly please someone help me out with the following piece of code to plot... |
H: How to work with different Encoding for Foreign Languages
I've got a Word Embedding File called model.txt. This contains 100 Dimensional vectors for over a million French words. These words contain accented characters such as é, â, î or ô.
Let me explain my problem with the following example:
Consider these two wor... |
H: Significance of Number of Calls and Reset Call in Ball Tree
Why does the Scikit Implementation has functions to reset and get number of calls?
How are these parameter important in Trees?
https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.BallTree.html#sklearn.neighbors.BallTree.reset_n_calls
AI: In... |
H: Smaller alternatives to sklearn that doesn't require scipy?
I am packaging my model for deployment in aws lambda which has a size limit of 250mb for all dependencies.
Sklearn, if you include its dependencies of numpy and scipy is a huge package.
Are there any alternatives to sklearn that don't require scipy that ar... |
H: What is the proper order of normalization steps before and after splitting data
I use a classification model on time-series data where I normalize the data before splitting the data into train and test. Now, I know that train and test data should be treated separately to prevent data leaking. What could be the prop... |
H: Classification model accuracy with ensemble methods
I came through this statement in a Machine Learning text book based on law of large numbers:
Suppose you build an ensemble containing 1,000 classifiers that are individually correct only 51% of the time (barely better than random guessing). If you predict the maj... |
H: What are some options to add or remove nodes from a multiclass classification model?
I'm building a classification model that will need to classify into one of many possible outputs. I know in advance that I will need to add and subtract nodes from the output layer as circumstances change. Please refer me to any re... |
H: how to adjusting already built ML predictive model
How can I continue machine learning model after predicting results?
What I mean by that is that I built a model for my 1 million records dataset, this model took around 1 day to get built.
I extracted the model results using Python and now I have a (function) that ... |
H: Choosing a distance metric and measuring similarity
I am trying to decide which particular algorithm would be most appropriate for my use-case.
I have dataset of about 1000 physical buildings in a city with feature space such as location, distance, year built and other characteristics etc. For each new data point, ... |
H: Feature selection inside Random Forest
I understood random forest is building a model with multiple decision trees,
Row sampling is based on bootstraping
My question is how feature selection is happening for each tree ?
Any help would be appreciated.
AI: A random subset of features than using the best split logic... |
H: What undergraduate degree should I take to get into data science?
I'm going to do an undergraduate degree next year. Data Science major is there but I was wondering if a statistics major with a minor in data science would be more valuable? Which degree would be more in demand?
I am also planning on doing as many in... |
H: Pandas - Sum of multiple specific columns
I created this script:
import pandas as pd
pd.set_option('display.min_rows', None)
pd.set_option('display.max_columns', None)
df = pd.read_excel('file.xlsx', sep=';', skiprows=6)
df = df.drop(['Position', 'Swap'], axis=1)
df.d... |
H: How to run Neural Net on GPU without python frameworks?
I coded a deep learning model from scratch in python(using numPy) without using any frameworks like keras or tensorflow. So far my model works fine but it runs on CPU.
How should i modify my code so as to run it on GPU instead?
AI: Most deep learning framework... |
H: How can I count the number of matching zero elements between two numpy arrays?
I have a function that returns the predicted accuracy of a time-series model. I have two equally-sized numpy arrays, one for the actual direction and one for the predicted direction. I'm classifying whether there is a change in the data'... |
H: How to know if classification model is predicting 1 or 0
I have used logistic regression to predict whether customer is good(1) or bad(0). I got the accuracy .80 . How do i know whether model predicted 1 or 0 .Is it related to parameter of model1.predict_proba(X_test)[:,1] (the 1 in the end in square bracket).
AI: ... |
H: What does it mean when the shape of input images is (600,64,64,3)?
While attempting an assignment, I found that shape of the input image was (600,64,64,3).
I thought 3 stood for the number of channels but it's listed as the 4th dimension.
What does this mean?
This is in reference to convolutional neural networks.
A... |
H: How to Transpose dataframe with custom output?
I have a data like this:
I want to transpose it like this in python:
AI: df.pivot_table(values='Confirmed',index['state','State_name'],columns='date',aggfunc='sum') |
H: how classification scores are interpreted?
I would like to know how to interpret classification scores (i am not sure about the word score or probability, please correct me). For example, for a binary classification positive values are labeled as 1, and -1 for negative ones. Now, is it fair to say that for a score... |
H: Conventions for dimensions of input and weight matrices in neural networks?
Im currently learning neural networks and I see conflicting decsriptions of the dimensions of weight and input matrices on the internet. I just wanted to know if there is some convention which more people use than the other.
I currently def... |
H: SMOTE oversampling for class imbalanced dataset introduces bias in final distribution
I have a problem statement where percentage of goods (denoted by 0) is 95%, and for bads (denoted by 1) it is 5% only. One way is to do under sampling of goods so that model understands the patterns properly for both the segment. ... |
H: Why are RNNs used in some computer vision problems?
I am learning computer vision. When I was going through implementations of various computer vision projects, some OCR problems used GRU or LSTM, while some did not. I understand that RNNs are used only in problems where input data is a sequence, like audio or text... |
H: How to visualize categorical data with numeric columns
I have data like as shown below
I would like to represent the above tabular data in a visual form.
However, the below graph may not work because my real data as 50K unique drug names.
So, is there any better way to represent this? Can you share some suggestio... |
H: Why isn't the neural network updated after every example in the dataset
Why the neural network is updating only after the whole batch passes?
AI: Weights get updated based on the number of examples you feed in a batch. This is because, a full pass(forward and backward) of matrix computations has to be completed in ... |
H: Appropriate objective function and evaluation metric when I DO care about outliers?
I am reading these two pages:
xgboost documentation
Post on evaluation metrics
I have a dataset where I am trying to predict future spend at the user level. A lot of our spend comes from large spenders, outliers. So, we care about t... |
H: Identify significant features in clustering results
I'm a student in Data Analysis, working on a data clustering exercise.
Two clusters have been identified based on a dataset with 40 features. To interpret and label these clusters, I'm wondering if there is a way to determine which features are the most determinan... |
H: Find the best interpolation method for missing observations
I have a database which has measurements of objects every day every hour.
However, some data is missing and I don't have measurements for all the hours.
in order to get over this challenge I have used different interpolations methods in order to create th... |
H: How to determine sample rate of a time series dataset?
I have a dataset of magnetometer sensor readings which looks like:
TimeStamp X Y Z
1.59408E+12 -22.619999 28.8 -22.14
1.59408E+12 -22.5 29.039999 -22.08
1.59408E+12 -22.32 29.039999 -21.779999
1.59408E+12 -22.38 29.16... |
H: How many ways are there to check model overfitting?
I am running xgboost on a regression classification problem where the model is predicting a score of how likely a gene is to cause a disease from 0-1.
I try to avoid overfitting in all the ways I can think of and the mean output of nested cross-validation is r2 0.... |
H: Does the performance of GBM methods profit from feature scaling?
I know that feature scaling is an important pre-processing step for creating artificial neural network models.
But what about Gradient Boosting Machines, such as LightGBM, XGBoost or CatBoost? Does their performance profit from feature scaling? If so,... |
H: List of numbers as classifier input
I am trying to use my data to predict the classes of the input. My data are as the following:
x1 = [0.2, 0.25, 0.15, 0.22] y = 1
x2 = [0.124, 0.224, 0.215, 0.095] y = 3
...
xn = [...] y = 2
The problem is that my data are just lists of numbers that do not have an order. I mean t... |
H: How to input LSTM output to MLP with concatenate?
I am having a training data set for a time-series dataset like below where my target variable is var1(t) which is the value of var 1 at time=t.
import numpy as np
import pandas as pd
train_df = pd.DataFrame(np.random.randint(0,100,size=(100, 16)))
train_df.columns=[... |
H: What do `loss` and `accuracy` values mean?
I'm using this:
Python version: 3.7.7 (default, May 6 2020, 11:45:54) [MSC v.1916 64 bit (AMD64)]
TensorFlow version: 2.1.0
Eager execution: True
With this U-Net model:
inputs = Input(shape=img_shape)
conv1 = Conv2D(64, (5, 5), activation='relu', padding='same', dat... |
H: Linear Regression Loss function for Logistic regression
I was attending Andrew Ng Machine learning course on youtube Lecture 6.4
He says what a cost function will look like if we used Linear Regression loss function (least squares) for logistic regression
I wanted to see such a graph my self and so I tried to plot... |
H: Dummy variable only for character value in a column (Neglecting float and integers)
My dataset consists of 3000 rows and 50 columns, out of which one column (ESTIMATE_FAMILY_CONTRIBUTION) contains all numerical value(around 2000 different values like 20,30,32....) but got one value as String e.g. 'No_information'.... |
H: Generative Adversarial Text to Image Synthesis
Can anyone explain the meaning of this line:
"Deep networks have been shown to learn representations
in which interpolations between embedding pairs tend to
be near the data manifold".
Reference: Section 4.3 of the paper Generative Adversarial Text to Image Synthesis
A... |
H: Where is the Backward function defined in PyTorch?
This might sound a little basic but while running the code below, I wanted to see the source code of the backward function:
import torch.nn as nn
[...]
criterion = nn.CrossEntropyLoss()
loss = criterion(output, target)
loss.backward()
So I went to the PyTorch Git... |
H: Is there a method to apply trained weights to a model with the same input shape and model architecture but different output shape?
I am developing a speaker identification model in Keras, and I have saved the weights from a trained custom model. Now, I am looking to use the trained weights to fine tune the model on... |
H: What is the best way to pick the optimized configuration from this dataset?
I have about 8000 configurations in an excel sheet. each configuration has four scores as seen in the image below. I would like to choose the best solution that has the highest lighting level score, lowest energy consumption score, the high... |
H: feature scaling xgbRegressor
I read for example in this answer:
Does the performance of GBM methods profit from feature scaling?
that scaling doesn´t affect the performance of any tree-based method, not for lightgbm,xgboost,catboost or even decision tree.
When i do feature scaling and compare the rmse of a xgboost ... |
H: Train/Test dataset and model
I would like to ask you how to work on train and test dataset. I have unlabelled data. They are short text (max 100 characters) and I would need to understand their sentiment.
To do this, I am manually assigning labels (1,0,-1). However I have more than 2000 text and I would like to fin... |
H: Modeling price vs demand
I have a dataset consisting of products, clients, price policy, discounts, quantities, and net sales. The task as put in words by the business is quantity vs price. I have noted a few observations from looking at the dataset :
Discounts: Discounts nullify the effect of any change in the Pr... |
H: why does my calibration curve for platts and isotonic have less points than my uncalibrated model?
i train a model using grid search then i use the best parameters from this to define my chosen model.
model = XGBClassifier()
pipeline = make_pipeline(model)
kfolds = StratifiedKFold(3)
clf = GridSearchCV(pipeline, p... |
H: One Hot Encoding for any kind of dataset
How can I make a one hot encoding for a unknown dataset which can iterate and check the dytype of the dataset and do one hot encoding by checking the number of unique values of the columns, also how to keep track of the new one hot encoded data with the original dataset?
AI:... |
H: SparseCategoricalCrosstentropy vs sparse_categorical_crossentropy
What is the difference between SparseCategoricalCrosstentropy and sparse_categorical_crossentropy ?
SparseCategoricalCrossentropy: Computes the crossentropy loss between the labels and predictions.
sparse_categorical_crossentropy: Computes the spars... |
H: How to perform a running (moving) standardization for feature scaling of a growing dataset?
Let's say that there is a function $r$
$r_n = r(\tau_n)$,
where $n$ denotes a so-called time-step of a system with an evolving state. Both $\rho$ and $\tau$ should equally influence $r$, and should therefore be scaled. The p... |
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