Questions tagged [preprocessing]

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Categorical features preprocessing for clustering

Can anybody tell me about best practise for clustering data with mixtured features both with categorical and continuous. I stumbled with a problem when I realised that for all metrics algorithms it's ...
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21 views

Should scaling be done for mixed data (categorical and numerical)?

My dataset contains 13 attributes consisting of 10 Numerical and 3 Categorical attributes and Target. It has 180 observations ...
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1answer
23 views

Pyspark Matrix Transformation

Let's assume I have the following dataframe in PySpark: ...
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7 views

Alarms prediction. Data preparation stage. Am I thinking in the right way?

Intro I have the dataset that contains the next information: Node Name (contains over 300 unique names) Event Date and Time (spotty one-year data) Alarm (3 alarm types) Clearing Time (the date ...
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1answer
27 views

Is it compulsary to normalize the dataset if doing so can negatively impact a Binary Logistic regression performance?

I am using raw data set with 4 feature variables to do a Binominal Classification using Logistic Regression Algorithm. I made sure that the class counts are balanced. i.e., an equal number of ...
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14 views

How to convert a non gaussian distribution into a gaussian destribution?

Suppose I have a dataset inwhich there are few dimensions that distribution over them is non gaussian and this means, skewness is nonzero (possitive or negative). This is caused by some outliers in my ...
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15 views

Multi-label Image Classification Neural Network

I am attempting to complete an old Kaggle competition on the iMet collection but having difficulties understanding how to preprocess the data. The data set consists of photos with a photo_id, a csv ...
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2answers
41 views

how to handle values that only appear once in a column?

Counting the values of a column using pandas I got the following result: ...
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1answer
17 views

Filling created feature with values

I'm trying to improve accuracy. I created a few new features based on old features. So I need to fill new feature's empty cell with same values in order to equaling shapes.Then, I tried it with median ...
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0answers
12 views

I have transformed my cyclic variables into sin-cos variables; will I need further normalization/standardization?

I have a dataset where there are both numerical, categorical and cyclic(month-quarter) variables. I will run a regression model, but I may also use Random Forest, XGBoost etc. So I will preprocess my ...
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13 views

Neural Network : possibility to get simple relationships from first layer?

Working on a data set with a lot of similar variables (for exemple : spending in something, spending in something else...), and suspected additive relationships, I was wondering about their agregation....
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1answer
9 views

Containing multicomponent data in rows or columns

I have been working with DNA sequences and compiled a table with features from those sequences. I have a column called Trimer, which contains strings. For some DNA sequences there is one trimer of ...
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38 views

binning high cardinality categorical features

one approach I have tried when preprocessing high cardinality categorical features (for example, US City) is to do a value count of all the values in the data, then take the top x most frequently ...
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1answer
26 views

Feature selection before or after applying filter in Time-series forecasting

I'm predicting ozone concentration based on meteorological variables and ozone value of the previous day. I applied savitzky golay filter to get rid of noise in the time-series dataset. My question ...
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41 views

Sparsify meaningful data following a Gaussian distribution

Let's say I have 1-D data following a Gaussian distribution. I want to extract from this database the meaningful information, that is the information that lies far away from the mean. One way to do ...
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8 views

prediction with un-obligatory features

I am pre-processing a real-world dataset with some features with missing values. these features are not mandatory - the user doesn't have to provide them. This means the values are not missing at ...
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8 views

fit transform with mysterious special chracters

I tried to make the bag of words ...
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2answers
41 views

How can I use mean normalization. Should I use it for numerical columns or categorical columns as well?

Should we normalize the categorical columns in our dataset? Or just the numerical columns?
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11 views

Preparing new samples for ML classifier with the same encoders

I was wondering what's the best way to preprocess new samples for my ML classifier. I have a raw data with about 3000 samples. I'm preprocessing it with some LabelEncoders and TargetEncdoders for ...
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11 views

Preprocessing data in image segmentation problem

I am implementing a research paper on image segmentation. Following are the image segmentation steps which are to be done before training its network- 1.Following image normalization is used- ...
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22 views

Which one of these is the most efficient way to model training data for a neural network that will play a snake-like game?

I am building an AI using a neural network that will play Tron against a human player. The game consists of a board with fixed width and height where each player can move at any direction (except for ...
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1answer
44 views

Positively skewed target label in regression

I have a dataset where the target label is positively skewed and produces a long tail, and currently I have a high residual on these values when experimenting with some linear, tree-based and neural-...
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2answers
34 views

How to export PCA to use in another program

I'm trying to write a random forest classifier for a very large dataset, as such as part of the pre-processing i have applied PCA to reduce from 643 features to 5 PC's. Is it possible to export these ...
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23 views

Does feature normalization improve performance of Hidden Markov Models?

For training a Hidden Markov Model (HMM) on a multivariate, continuous time series, is it preferable to scale the data somehow? Some pre-processing steps may be: Normalize to 0-mean and unit-variance ...
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1answer
24 views

Is there any library available for balancing imbalanced text dataset?

I have a text dataset similar to newsgroup dataset, the problem with the dataset is that it is highly imbalanced. So is there any readily built library that will do upsampling or downsampling with a ...
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8 views

How would one reduce dimensionality/covariance in a dataset with nonlinearly covariant variables? Is M-SSA a no-go?

I am familiar with the Principal Component Analysis method of covariance and dimensionality reduction. I am considering using its multivariate time series brother, Multivariate Singular Spectrum ...
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3answers
71 views

One Hot Label Encoding Scikit_learn convert back to Data Frame

I have a data frame with 4 features and 1 target. The 4 features are 3 categorical and 1 numerical. I created X which is a new data frame for the 3 categorical features. I use one hot label encoding ...
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37 views

preprocessing : Predicting with Multiple+Multivariate+Multitrend time series data

I am trying to predict the value of a variable in a multivariate time series; of which I have multiple time datasets (one system = one dataset containing 10 variables in time and average 120,000 rows) ...
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17 views

Handling collection of featurevectors for classification

I have a data set where devices are represented by a collection of variables. These variables consist of several properties like a name, datatype, driver, limit values, etc. (mixed data; quantitative ...
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36 views

Pre-processing data to make predictions on deployed Sklearn model

I am new to Machine Learning. I have trained a ML model on the Diamond Prices Dataset to predict the price of a diamond given it's features (carat, cut color, clarity, etc...) I have used pickle to ...
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18 views

Feature vector of linear model

I read this paper that applies logistic regression to a dataset generated from a simulation they created. The dataset contains a set of binary vectors (called challenges) that looks like this: ...
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19 views

ZCA: Covariance of Large Image Database

I have an overall question if my method is sound, so please bear with me in this description :). I have a large image database and I wanted to create a preprocessing step using ZCA. The issue is that ...
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2answers
123 views

sklearn.preprocessing.MinMaxScaler: non-broadcastable output shape error

Why I am getting the following error: ValueError: non-broadcastable output operand with shape (1,1) doesn't match the broadcast shape (1,2) While executing: ...
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1answer
342 views

nltk's stopwords returns “TypeError: argument of type 'LazyCorpusLoader' is not iterable”

While trying to remove stopwords using the nltk package, the following error occurred: ...
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11 views

How to avoid the alternating conditional expectations from transforming most of the response data to very close values?

I am applying alternating conditional expectations (ACE) in a forward stepwise manner, similarly to the authors of the original ACE paper. My dataset has 103 predictor variables $x_i$, one response ...
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5answers
96 views

Large no of categorical variables with large no of categories

I'm working on a binary classification problem where the dataset is slightly imbalanced (30% class 0 | 70% class 1). Most of my features are categorical with large number of categories. For example: ...
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1answer
22 views

Input Normalization for Transfer Learning

If I am training a deep neural net with input features that are physical in nature (e.g. temperature, precipitation, etc), and I want to be able to perform some kind of transfer learning where I train ...
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0answers
22 views

Labeling audio dataset [duplicate]

I would like to try and create my own audio dataset which I can then use to train machine learning models for classification. The data that I've gathered consists of multiple long audio files of ...
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1answer
32 views

Searching prediction from 4 datasets

The fourth dataset contains (train_data, test_data, previous_data, and information_history_data). The goal is to search for a user's rating on the loan to the bank. ...
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2answers
68 views

Understanding missing values in dataset

I have recently worked with a dataset of real estate transactions with missing entries for some features. For instance, GarageYrBlt (year when a garage was built) ...
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5 views

Class-inflated factor. At what point do we decide to remove it?

If we have a dataset with a few string-type factors that have a lot of one class, at what point do we decide to remove the said class? This question just came to mind since I was practicing on a ...
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1answer
40 views

Scaling features separately?

I have some features which are in the thousands, which I scale to the max values of these. This solves the general scaling problems, as well as preserves an important absolute value relationship ...
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0answers
48 views

Data normalization of count data for neural networks

I have a sparse matrix of count data that I'm using as input to a neural network. I know, usually, the input data should be normalized (e.g. via min-max scaling, $z$-score standardization, etc.). But ...
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2answers
79 views

Best way to scale across different datasets

I have come across a peculiar situation when preprocessing data. Let's say I have a dataset A. I split the dataset into A_train ...
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202 views

One hot encoding as input to recurrent neural networks

I'm trying to predict next label in a pattern based on previous labels using recurrent neural network. In total I have 100 labels Example of input pattern: ...
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21 views

Image Labeling Tool

Problem Now I am working on a project to identify the subjects in the image is man, woman or ...
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1answer
29 views

Suggestion on Preprocessing dataset

I am trying to preprocess my dataset and needs some suggestion on it. The training data shape is : (166573, 14) The distribution of features : As you can see, only the first 4 columns go to ...
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138 views

Using Keras.utils.Sequence on out of memory dataset

I'm trying to implement a sub-class of keras.util.sequence so that i can load data faster into the function fit_generator. Currently I have a multi-variate time series (multiple features) file which ...
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21 views

How can I transform (pre-process) pure count data for PCA analysis?

If 12 covariates of data are all count data (looks like a Poisson dist with the highest peak at 0), what are reasonable pre-processing methods that might make PCA more effective?
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1answer
18 views

One attribute includes another attribute

I have a telecom dataset that has many attributes, among these attributes, there is "Voice mail plan" attribute that takes yes or no, and another attribute is "voice mail calls" which has many values, ...