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H: Why spectral clustering results in disjointed cluster? I'm working on a project where I have to dynamically cluster the position of objects with respect to one coordinate. So I'm essentially dealing with subsequent frames and each frame represents a one-dimensional dataset. The intuition behind clustering is to for...
H: In datasets, why don't we represent nominal values that are part of a scale with numbers? I am trying to pre-process a small dataset. I don't understand why I am not supposed to do the thing I explained below: For example, say we have an attribute that describes the temperature of the weather in a set of 3 nominal ...
H: Time series with additional information Given a time series with job-submission counts, how can I predict which certain features about the jobs? I need to predict how many jobs and which jobs arrived in some system. Using pandas.groupby, I've sliced the data into 15 minutes intervals. I can predict how many jobs ...
H: Will the MAE of testing data always be higher than MAE of training data? On the Kaggle Course Page the chart below shows that MAE of testing data is always higher than MAE of training data. Why is this the case? Is it only limited to DecisionTreeRegressor model? Or the graph is wrong and in practice the MAE of test...
H: How to find the various matrix sizes in designing a CNN I am trying to understand CNN especially the maths and working mechanism using Matlab as the coding language. I have few confusion regarding the concept and the associated programming and will be immensely grateful for an intuitive answer. Below is the structu...
H: Which kind of model is better for keyword-set classification? There exists a similar task that is named text classification. But I want to find a kind of model that the inputs are keyword set. And the keyword set is not from a sentence. For example: input ["apple", "pear", "water melon"] --> target class "fruit" i...
H: looking for approaches to detecting outliers in individuals unequal sequential time series I am looking for approaches related to outlier detection in time series. Example: A person visits hospital overtime on multiple bases and there are some measurements made (bmi, blood_pressure, stress_level) at each occasion. ...
H: How should I approach such classification problem where the input is an array of integers? I am training a model for predicting a number between 0 to 10 in this case. These are the number of roots of a polynomial. The input array for the number of polynomials is the coefficients of that polynomial from $x^n$ to con...
H: Why cant RNN learn long term dependencies=? In Colah's blog, he explain this. In theory, RNNs are absolutely capable of handling such “long-term dependencies.” A human could carefully pick parameters for them to solve toy problems of this form. Sadly, in practice, RNNs don’t seem to be able to learn them. Th...
H: How to interpret dummy variable in ML prediction? I am working on a binary classification problem where I have a mix of continuous and categorical variables. Categorical variables were created by me using get_dummies function in pandas. Now my questions are, 1) I see that there is a parameter called drop_first whic...
H: Rescaling each image Individually with keras I am a beginner working on a simple CNN to classify X-ray detector images. Due to source intensity, all images have different max values. I want to use ImageDataGenerator to rescale those images to be in range [0,1], but cant't find a way to do that for each individual i...
H: Terminology - regression with one output and multiple output variables I am trying to predict the response when the input is represented by Fourier transform. These form the features and are typically represented as a vector, $x_1,x_2,...,x_d$ where $d$ is the length of the fourier transform. Based on my understand...
H: XGBoost Feature Importance, Permutation Importance, and Model Evaluation Criteria I have built an XGBoost classification model in Python on an imbalanced dataset (~1 million positive values and ~12 million negative values), where the features are binary user interaction with web page elements (e.g. did the user scr...
H: How to assess that a cross entropy based model has converged I have a question regarding cross entropy convergence using Stochastic Gradient Descent. I am a little bit confused about how the convergence should be assessed. Should we treat the model as converged if the loss hit minimum on any single example or may b...
H: Is there a paper accomplishing finding physical law from observation without premade perception, using machine learning? For example: Isaac Newton finds law of universal gravitation just by looking a falling apple, without any premade perception of that phenomenon. Is it possible to accomplish that kind of discover...
H: How can I compare my regressors? I am trying to build a regressor for a dataset which gives info about students' school performance and the probability of getting admitted in the University of their choice. The first 5 observations look like this : GRE_Score TOEFL_Score Uni_Rating LOR CGPA Research Chance...
H: Val_accuracy (val_acc) very low We have a data set that is converted from signal data to video. We want to classify these images using convolution. We tried many different methods but val acc is consistently low. Training accuracy is 99% and val_acc is 40%. We need your help in this respect. Thank you weight_decay=...
H: Tableau: Clustering based on value-range for map coloring Is there a possibility to cluster coloring for certain statistical ranges? This is what I have been able to achieve so far. AI: You can, as an example, create a binned field for the measure. The value range can be specified in the tooltip. I used a single co...
H: Does cross_validate in scikit-learn automatically fits and train the model? # from the titanic dataset X = df.drop(columns="survived") y = df.survived scoring = ['accuracy','precision','roc_auc','f1',] from sklearn.model_selection import cross_validate from sklearn.linear_model import (LogisticRegression) def model...
H: Evaluating a IR system (Precision and Recall) I am studying by now IR system, in the field of valuation of IR system outputs related to a specific query but I need some help to understand it properly. My book states that when an IR system has to be evaluated, we need a test document collection, a set of query exam...
H: How to explain the connection between the input layer and H1 of this CNN Architecture? I am currently reading the paper proposed by LeCun et al. for handwritten zip code recognition. There is this figure below visualizing the CNN architecture. But I do not really understand how the connection between Layer H1 and i...
H: About the maximum likelihood, when we convert the maximization problem into minimization, why we take the negative? On page 12, we take $log$ on both side. $\max_{\boldsymbol{w}}L\boldsymbol({w})=\max_{w}\displaystyle\prod_{n=1}^Np(t^{(i)}|x^{(i)};\boldsymbol{w})$ $\ell(\boldsymbol{w})=-logL(\boldsymbol{w})$ $\ \ ...
H: 1: 10 rule in logistic regression - EPV I have a dataset with 4712 records. Label Yes - 1558 records and Label No - 3554 records. I read online that 1:10 rule is based on the frequency of lower occurring class. In my case, frequency of lower occurring class is 1558 According to 1:10 rule, am I right to understand t...
H: Batch Normalization vs Other Normalization Techniques In the context of neural networks, I understand that batch normalization ensures that activation at each layer of the neural net does not 'blow-up' and cause a bias in the network. However, I don't understand why it would be used as opposed to other normalizatio...
H: Does it make sense to expand word embeddings so that each array index is a feature input or should the embedding itself be a model input? If you are building a DNN, say, with two layers, and you want to use embeddings as one of your feature inputs, what's the best way to input the embedding? I'm trying to understan...
H: feature selection using genetic algorithm in Python? I have a dataset of 4712 records and 60+ features working on a binary classification problem. I already tried out all the feature selection approaches like filter, embedded and wrapper but am just curious to learn and try genetic algorithm for feature selection. ...
H: Why replay memory store old states and action rather than Q-value (Deep Q-learning) Here is the algorithm use in Google's DeepMind Atari paper The replay memory D store transition (old_state, action performed, reward, new_state) The old_state and the performed action a are needed to compute the Q-value of this acti...
H: PyTorch: How to use pytorch pretrained for single channel image If I have to create a model in pytorch for images having only single channel. How can I transform my model to adopt to this new architecture without having the need to compromise the pre-trained weights on which it has been trained upon. AI: I came acr...
H: Auto ML vs Manual ML for a project I recently was introduced to a AUTO ML library based on genetic programming called tpot. Thanks to @Noah Weber. I have few questions 1) When we have AUTO ML, why do people usually spend time on Feature selection or preprocessing etc? I mean they do at-least reduce the search space...
H: How to select best feature set and not ranking for tree based models? I am currently using feature selection approaches like filter, wrapper, embedded etc. All these methods give different set of features and I rank them based on their frequency of occurrence in other feature selection approach. Ex: If Age occurs i...
H: Why joint probability in generative models? I have been reading about generative models for past few days and there's one question that's bugging me. Many sources(Wiki, Google dev article) mention that generative models try to model the joint probability distribution p(x, y) and that to generate new samples we samp...
H: R: Error when using Aggregate function to compile monthly means into yearly means Disclaimer: I'm extremely new to R and have been getting by with using google as my professor. I have a somewhat large collection of monthly values over a period of several years from several different locations. I am attempting to us...
H: How does L1 regularization make low-value features more zero than L2? Below formulas, L1 and L2 regularization Many experts said that L1 regularization makes low-value features zero because of constant value. However, I think that L2 regularization could also make zero value. Could you please explain the reason w...
H: Different encoders applied to a dataset I have a dataset which have both categorical features with high cardinality (>8000) and low cardinality (4 or 5). Would that be ok to encode the high cardinality ones with one encoder (target encoder, for example) and the others with low cardinality with another encoder (one...
H: Which metric to choose for tracking model performance? I am working on a binary classification problem with class proportion of 33:67. Currently what I am doing is running multiple models like LR,SVM,RF,XgBoost for classification. RF and Xgboost models perform better. However I am reading online that AUC, F1-score ...
H: Multi-label classification using output quantization Problem statement It's a fact that in order to train the network for multilabel-dataset, a one-hot-vector output is usually used. Example: dog [1 0 0] cat [0 1 0] rabbit [0 0 1] Consequently, we're increasing size of the weight matrix as we...
H: How to incorporate keyboard positions on character level embeddings? I am working with NLP and have character level embeddings. I have embeddings learned from Wikipedia text. Now, I want to learn embeddings from chat data (where misspellings and abbreviations are way more common). Usually, the character n doesn't f...
H: NLP: How to group sub-field into fields? Suppose I have a list of strings that captures a sub-field of academic research and would like to group them as higher-level fields. For example, 'Quantum Mechanics' => 'Physics' 'Abstract Algebra' => 'Mathematics' .... My understanding is that standard NLP techniques m...
H: ML Project - Achieve 2 Objectives I have a dataset with 5K records focused on binary classification. I am posting it here to seek your suggestions on project methodology Currently what is my objective is 1) Run statsmodel logistic regression to find risk factors that influence the outcome 2) Then build a predictiv...
H: Is there any difference between a weak learner and a weak classifier? While reading about decision tree ensembles Gradient Boosting, AdaBoost etc. I have found the following two concepts weak learner and weak classifier. Are they the same? If there is any difference what is it? AI: A weak learner can be either a cl...
H: Best clustering algorithm to identify clusters and determine the closet cluster each individual response is near? I have a survey where each question is related to a different 'shopper' type (there are 5 types so 5 questions). Each question is either binary (True/False) or scale based. IE: 1. Do you like to shop at...
H: why we have run make.sh file initially in darknet and YOLO object detection? In have seen a couple of text and objection detection algorithm wher the first step everyone dose is to install cython and run a make.sh file . why we have run make.sh file initially in darknet/YOLO object detection ? Below are the 2 links...
H: What to choose: an overfit model with higher evaluation score or a non-overfit model with lower one? For lack of a better term, overfit here means a higher discrepancy between train and validation score and non-overfit means a lower discrepancy. This "dilemma" just showed in neural network model I've recently worki...
H: Can colored images have more than 3 channel values? I was reading this well-known paper and noticed something in figure 1 below: It says in the caption (The number of channels is denoted on top of the box). You can see that the number of channels is ranging from 1 up to 1024. I am confused here because it is known...
H: Threshold to consider new feature as a new finding to a model? I am working on binary classification problem with 5K records and 60 features. Through feature selection, I narrowed it down to 14 features. In existing literature, I see that there are well-known 5 features. I started my project with an aim to find new...
H: How neural style transfer work in pytorch? I am using this pytorch script to learn and understand neural style transfer. I understood most part of the code but having some hard time understanding some parts of the code. In line 15 Its not clear to me how model_activations work. I made a sample style tensor of the ...
H: What is a manifold for Unsupervised Learning? I've been watching Dr. G. Hinton lectures on Neural Networks in Machine Learning, and in one of the lectures he explains what the goals of Unsupervised Learning are. I am having trouble understanding the part where high-dimensional inputs such as images live on or near...
H: Maximize one data point I am completely new to data science and looking to narrow down the search and reduce the learning curve required to solve problems like the one given below I have a data set with 7 columns , Column A(all positive decimal) is the data point I want to maximize. Column B and C are boolean val...
H: Machine learning dataframe dimension concept vs NumPy dimension From Machine Learning for Absolute Beginners: A Plain English Introduction: Contained in each column is a feature. A feature is also known as variable, a dimension or an attribute - but they all mean the same thing. From here (the supplement file for...
H: Why and how to match variables in logistic regression? I have a dataset of ~4.7K records focused on binary classification with 60 features. class 1 is of 1554 records and class 2 is of 3558 records. Now I would like to find the risk factors that influences the outcome which is disease present or not. This is a supe...
H: TensorFlow Sigmoid activation function as output layer - value interpretation My TensorFlow model has the following structure. It aims to solve a binary classification problem where the labels are either 0 or 1. The output layer uses a sigmoid activation function with 1 output. model = keras.Sequential([ layers...
H: How to use random forest model to new data? I am new to this Data Science field. I have a question to apply Random forest to new data. I have this table. Y prop_A prop_B A 0.8 0.2 A 0.7 0.3 B 0.5 0.5 B 0.4 0.6 B 0.1 0.9 I assumed that if the proportion of the group is high, chances are hig...
H: Multilingual Bert sentence vector captures language used more than meaning - working as interned? Playing around with BERT, I downloaded the Huggingface Multilingual Bert and entered three sentences, saving their sentence vectors (the embedding of [CLS]), then translated them via Google Translate, passed them throu...
H: TensorFlow / Keras: What is stateful = True in LSTM layers? Could you elaborate on this argument? I found the brief explanation from the docs unsatisfying: stateful: Boolean (default False). If True, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the...
H: How to use scikit metrics for a statsmodel or vice versa? Am working on binary classification problem with 5K records. Label 1 is 1554 and Label 0 is 3558. I did refer this post but not sure whether it is updated now or anyone has any way to compute this metrics Currently I use logit model as shown below model = sm...
H: Which feature selection technique to pickup(Boruta vs RFE vs step selection) I have data with 103 columns. I would like to understand which algorithm is best for feature selection and what may be the logic to call any feature as best. I run below feature selection algorithms and below is the output: 1) Boruta...
H: sklearn SimpleImputer too slow for categorical data represented as string values I have a data set with categorical features represented as string values and I want to fill-in missing values in it. I’ve tried to use sklearn’s SimpleImputer but it takes too much time to fulfill the task as compared to pandas. Both m...
H: In which cases are non-linear learning methods preferred than logistic regression in classification problems? We know that neural networks and other learning methods can have better performance relative to logistic regression in some non-linear classification problems. But, it is known too that logistic regression ...
H: Algorithm selection rationale (Random Forest vs Logistic Regression vs SVM) I want to understand the criteria of selection of ML algorithms, i.e., what are the guidelines on which algorithm to be selected in which case? The reasons I know are: Logistic regression to be picked in case we want to advise the impact o...
H: How to approach TF-IDf based analysis? Problem statement : We have documents with list of words in them. Overall these documents are classified into 2 group (say, good quality vs bad) docs - doc1 = [w1,w2,w3,w4] doc2 = [w4,w3,w3,w4] doc3 = [w2,w4,w8,w1] doc4 = [w5,w4,w0,w9] doc group - good_grp = [doc2, doc1] bad_...
H: How to save and load model from unsupervised learning? [Beginner] Sorry if this is dumb question. I am following the model from this article and below. import pandas as pd import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from sklearn import datasets from sklearn.decomposi...
H: How are samples selected from training data in Xgboost In Random Forest, each tree is not fed with the full batch of training data, only a sample. How does this work for Xgboost? If this sampling happens as well, how does it work for this ML algorithm? AI: In Gradient Boosting the simple tree is built for only a ra...
H: Does variance and standard deviation both measure how spread out the numbers are? I've heard from different sources that, standard deviation measures how spread out the numbers are. But I've also heard the same for variance. Is it technically correct to say this statement for both std and var? AI: Yes it is. Standa...
H: Clustering algorithm which does not require to tell the number of clusters I have a dataframe with 2 columns of numerical values. I want to apply a clustering algorithm to put all the entries into the same group, which have a relatively small distance to the other entries. But which clustering algorithm can I use, ...
H: what are the steps in adaboosting? I went through adaboost tutorial and below are my simplified understanding: Sample weight of equal value is given to all sample in dataset. Stumps are created which uses only one feature from data set. Using total error and sample weight stump importance is calculated. Samples we...
H: Classify pdf files - image approach vs. text approach I'm about to start a project with the objective to classify PDF-documents. I'm wondering if there's a best practice approach to tackle this problem. Concretely I'm wondering if one of the following two approaches performs usually better: use an OCR reader to co...
H: Including the validation file in the training process after tunning Should I include the validation file in the training process after finishing the tuning process (e.g. searching for params using the validation file)? AI: It depends on the distribution of the train, valid and holdout/test set. There are a couple o...
H: How to recognize product based on image using neural network? Company has many products in their offer (some about 100,000), some of these are very similar to each other. In database there is available only one image per product. Company want to make possible to recognize product based on video camera and display ...
H: What is purpose of the [CLS] token and why is its encoding output important? I am reading this article on how to use BERT by Jay Alammar and I understand things up until: For sentence classification, we’re only only interested in BERT’s output for the [CLS] token, so we select that slice of the cube and discard ev...
H: How to convert Scikit Learn logistic regression model to TensorFlow I would like to use existing Scikit Learn LogisticRegression model in the BigQuery ML. However, BQ ML currently has a hard limit of 50 unique labels and my model needs to handle more than that. BQ accepts TensorFlow models, which do not seem to ha...
H: Using Majority Class to Predict Minority Class Suppose I want to train a binary model in order to predict the probability of who will buy a personal loan and in the dataset only 5 percent of the examples are people who marked as bought a personal loan. So, in this scenario maybe I can leverage downsampling or upsam...
H: R: Producing multiple plots (ggplot, geom_point) from a single CSV with multiple subcategories I have a collection of bacteria data from approximately 140 monitoring locations in California. I would like to produce a scatterplot for each monitoring location with the Sampling Date on the Y-axis and the Bacteria Data...
H: Using a GAN discriminator as a standalone classifier The goal of the discriminator in a GAN is to distinguish between real inputs and inputs synthesized by the generator. Suppose I train a GAN until the generator is good enough to fool the discriminator much of the time. Could I then use the discriminator as a clas...
H: Kernel selections in SVM I want to understand the kernel selection rationale in SVM. Some basic things that I understand is if data is linear, then we must go for linear kernel and if it is non-linear, then others. But the question is how to understand that the given data is linear or not, especially when it has ma...
H: Convert Numpy array with 'n' and 'y' into integer array of 0 and 1 I have a NumPy array of strings: 'n', 'y', wanna convert it into integer array of 0, 1, how to convert it? imp = SimpleImputer(missing_values=np.nan, strategy='most_frequent') X = imp.fit_transform(X) X array([['n', 'y', 'n', ..., 'y', 'n', 'y'], ...
H: Should I create a separate column for each Id value in a feature column or can I use the feature column as it is? I am working on developing a model for predicting, revenue that a movie will make. One of the features in the training set contains id of the series that a movie belongs to.Say, Star Wars series has Id ...
H: KeyError: 'i' and Error I am implementing the codes for https://www.kaggle.com/eray1yildiz/using-lstms-with-attention-for-emotion-recognition which is an emotion analysis from the text. I am having some errors when I am Encoding my samples with corresponding integer values. The following codes are: X = [[word2id[...
H: How to balance class weights correct for a CNN in Keras, given an unbalanced data set? I want to use class weights for training a CNN with a imbalanced data set. The question arise if the sum of the weights of all examples have to stays the same? My previous plan was to use the function compute_class_weight('balanc...
H: In survival analysis, which is the correct way to introduce a variable which changes the survival rate but occurs at different times? I am making a survival analysis with a cox regression with proportional hazards, we want to analyze wheter the introduction of a phenomenon influences the time until the death of an...
H: Resampling with Python SMOTE I am trying to do a simple ML re-sampling approach after the train-test split. However when I do this, it throws the below error. Can you please help me understand what this error is about? KeyError: 'Only the Series name can be used for the key in Series dtype mappings.' The code is g...
H: Machine learning methods for panel (longitudinal) data I have a panel data set, for example: obj time Y x1 x2 1 1 0.1 1.28 0.02 2 1 0.11 1.27 0.01 1 2 -0.4 1.05 -0.06 2 2 -0.3 1.11 -0.02 1 3 -0.5 1.22 -...
H: How to impute Missing values not the usual way? I have a dataset of 4712 records working on binary classification. Label 1 is 33% and Label 0 is 67%. I can't drop records because my sample is already small. Because there are few columns which has around 250-350 missing records. How do I know whether this is missing...
H: Extracting name, date and total from a set of heterogeneous receipts So, this is how the problem goes: I am trying to extract information from scanned receipts like this, What I have been told is that I would get the textual data from a OCR software, so in short I will be working with a textual version of the imag...
H: How to evaluate performance of a new feature in a model? I am working on a binary classification where I have 4712 records with Label 1 being 1554 records and Label 0 being 3558 records. When I tried multiple models based on 6,7 and 8 features, I see the below results. Based on the newly added 7th or (7th & 8th) fe...
H: Why results of statsmodel logreg is different from scikit-learn logreg? I am trying to do a binary classification. I have only 6 input variables and one output variables. Label 1 is 1554 records and Label 0 is 3558 records. As you can see below, the metrics that I get from these two are different. I am not sure wha...
H: Why ML model produces different results despite random_state defined? And how to set global random seed for sklearn I have been running few ML models on same set of data for a binary classification problem with class proportion of 33:67. I had the same algorithms and same set of hyperparamters during yesterday and ...
H: Compare between similar and dissimilar couples of instances I label couples of similar and dissimilar instances based on user behavior. each instance has a lot of features. I have few ways of labeling the couples. I know want to evaluate which of the label methods produce the most homogeneous distribution in the gr...
H: What should I use as training data for base (level 1) classifiers in ensembling? Can I just take all training data that I have, train the base models on them and then take their results and use them for training level 2 model? Is this a good practice, or should it be done differently? AI: You can do that, but your ...
H: Multiclassification Error: NotFittedError: This MultiLabelBinarizer instance is not fitted yet After picking the model, when I try to use it, I am getting error - "NotFittedError: This MultiLabelBinarizer instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator." X = <train...
H: How does BERT and GPT-2 encoding deal with token such as <|startoftext|>, As I understand, GPT-2 and BERT are using Byte-Pair Encoding which is a subword encoding. Since lots of start/end token is used such as <|startoftext|> and , as I image the encoder should encode the token as one single piece. However, when I...
H: Need of maxpooling layer in CNN and confusion regarding output size & number of parameters In my CNN architecture for binary classification, I have 2 convolutional layers, 2 maxpooling layers, 2 batchnormalization operations, 1 RELu and 1 fullyconnected layer. Case1: When the number of channels, $d=1$: In the firs...
H: Forward pass vs backward pass vs backpropagation As mentioned in the question, i have some issues understanding what are the differences between those terms. From what i have understood: 1) Forward pass: compute the output of the network given the input data 2) Backward pass: compute the output error with respect t...
H: NA in LR model summary(R) So, i was trying to improve mr LR model performing multiple linear regression on a dataset. I had a categorical variable region Region(variable): Midwest Northeast South West I made dummy variable for each of them and it did improved my model a bit. Previous Model Summary After adding ...
H: PCA Regression Problem I have a regression problem whereby my data has 21 features and I wish to apply dimensionality reduction using PCA. As far as I know, all the tutorials I have seen so far use PCA for classification problems. I did do PCA for regression but I am un-able to display the nice scatterplots that sh...
H: Previous work Replication and Research ethics Ask Question I am very much concerned about biding by research ethics in my work, especially issues to do with plagiarism. I come across a recent research paper in my field of study that applies state-of-the-art tools (deep learning architectures) in their work using a ...
H: (pre-trained) python package for semantic word similarity I am searching for a python package that calculates the semantic similarity between words. I do not want to train a model (what most packages seem to offer) - the package should have been pre-trained on ideally thousands of natural language books and documen...
H: Fastest way to relearn machine/deep learning I hope I came to the right place to ask this question. Back when I was at collage I studied machine and deep learning in-depth. My whole programme was based on those areas. I knew all underlying maths, even today I know how to derive backpropagation for any feed-forward ...
H: Implementing "full convolution" to find gradient w.r.t the convolution layer inputs I've been trying to implement "full convolution" w.r.t to convolution layer inputs. According to this article, it looks like this: So, I wrote this function: def full_convolve(filters, gradient): filters = np.ones((5,5)) g...
H: Random Forest prediction fails due to unseen Features I have trained a Random Forest Model on some dataset and like to predict outcomes on other data which were not seen in training. When doing this, I get ValueError: Number of features of the model must match the input. Model n_features is 12 and input n_features...
H: Why continuous features are more important than categorical features in decision tree models? I have both categorical and continuous features in my prediction model and want to select (and rank) most important features. I have converted all categorical variables into dummy variables using one hot encoding (for bett...