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H: How to do feature analyzing : pandas groupby(). mean
I'm analyzing Titanic data from Kaggle1 with a kind of guiding book. In the book, feature analyzing about the relationship between Pclass data and Survived data is done like below.
train_set = pd.read_csv('train_csv)
fig = plt.figure(figsize=(12,4))
ax1 = fig.add... |
H: Is it necessary to tune the step size, when using Adam?
The Adam optimizer has four main hyperparameters. For example, looking at the Keras interface, we have:
keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)
The first hyperparameter is called step size or learning ... |
H: Connect a dense layer to a LSTM architecture
I am trying to implement an LSTM structure in plain numpy for didactic reason. I clearly understand how to input the data, but not how to output.
Suppose I give as inputs a tensor of dimension (n, b, d) where:
• n is the length of the sequence
• b is the batc... |
H: tensorflow simple regression nan after >29 observations
I have code as below. If the number of data points changed to any number above 30 (example 40) then i get nan for values of final_slope , final_intercept
why?
For 25 examples it runs fine. I am using a cpu version on tensorflow on my windows machine.
The numb... |
H: Why do I not get 100% Accuracy with KNN with $K=1$
I am playing with KNN on the Iris Dataset
I expected to get 100% accuracy with $K=1$ since every point should predict itself based on the Voronoi volume around it created by the KNN algorithm.
However using Scikit Learn I do not find this result. Here is my code.... |
H: How can I get my trained model ready for production
I successfully trained my model using the sklearn's multiple linear regression. This is the code I used:
import pandas as pd
dataset = pd.read_csv('C:\\mylocation\\myfile.csv')
dataset2 = pd.get_dummies(dataset)
y = dataset.iloc[:, 31:32].values
dataset2.pop('Tar... |
H: Is there any implementation of Extended Isolation Forest algorithm in R/Python?
I am using isofor package for regular Isolation Forest but I came by an article about Extended Isolation Forest and need your advise which package has this functions implemented in R/Python.
AI: There is a package on Github called "Exte... |
H: How can I use a class variable with many possible values in logistic regression?
I am attempting to build a logistic regression model that determines the probability of an outcome based on a set of independent variables.
For context, the data is based on a project in which sales representatives and branch managers ... |
H: What are your thoughts on SKLearn's dismissal of GPUs for machine learning?
SKLearn has this broad claim in its FAQs:
Outside of neural networks, GPUs don’t play a large role in machine learning today, and much
larger gains in speed can often be achieved by a careful choice of
algorithms.
Anyone care to add c... |
H: What does it mean when someone says "Most of the data science algorithms are optimization problems"
I was trying to understand the Gradient Descent algorithm from this article and the author says
Most of the data science algorithms are optimization problems
I come from software engineering background trying to g... |
H: How to represent an image as state in a Q-table
I'm trying to do Q-learning with the Atari games using the gym python's package.
I want to use the image as the state of my algorithm, but I came up with a doubt: Is the state all the possibilities for all the pixels of the image?
I've seen many Q-learning implementat... |
H: Conjugated gradient method. What is an A-matrix in case of neural networks
I am reading about conjugated gradient methods to understand how they exactly work. I understand that a pair of vector $u$ and $v$ are conjugated with respect to $A$ if $u^TAv=0$. I also read that $A$ is symmetric, positive definite matrix.
... |
H: Output of classifier.predict Tensorflow extract probabiltity
When I do a prediction with my DNN clasifier I get a dictionary like this.
{'probabilities': array([9.9912649e-01, 8.7345875e-04, 8.5633601e-12], dtype=float32), 'logits': array([ 12.641698, 5.599522, -12.840958], dtype=float32), 'classes': array(['0']... |
H: How to create column for my csv file in python
I have a CSV file having these values (without column):-
I:30n
J:0n
J:0n
U:1000n
C:0n
I:12n
I:10n
I:10n
I:10n
I:10n
I want to add a column name for these rows values.
Suppose all I (i.e: I:30n, I:12n, etc)value record should be in
one column and likewise all J (i.e J... |
H: Who wrote the formula for gini importance/sklearn's feature importance score?
I've been looking for a paper where the Gini importance was first proposed, but I am not sure if this is actually how it came to be.
Here's the formula I am familiar with and am looking to find in a paper:
$$\frac{N_s}{N_t} * \left(i - \f... |
H: How to create a new column based on two other columns in Pandas?
I am searching for a way to create a new column in my data. I have tried using iterows() but found it extremely time consuming in my dataset containing 40 lakh rows. So here is what I want.
Consider I have 2 columns:
Event ID, TeamID ,I want to find t... |
H: Scaling values for LSTM
I have the following time series data set
Each row is a unique Item, and each column shows the amount purchased per day. There are a total of 33 columns.
I'm taking the first 32 columns(leaving out the last column, which will be my target) as my training set, and the last 32 rows (leaving o... |
H: how to evaluate feature quality for decision tree model
Most of the tutorials assume that the features are known before generating the model and give no way to select 'good' feature and to discard 'bad' ones.
The naive method is to test the model with new features and see how the new results change compared to the ... |
H: What is an intuitive explanation for the log loss cost function?
I would really appreciate if someone could explain the log loss cost function And the use of it in measuring a classification model performance.
I have read a few articles but most of them concentrate on mathematics and not on intuitive explanation an... |
H: Difference between 1x1 Convolution and TimeDistributed(Dense())
Are these lines of code equivalent in Keras? From a few runs, they seem to be, and also intuitively since the channels dimension of my data is 1, my understanding is that a fully connected acts like a convolutional layer. Is one better computationally ... |
H: How to identify potential customers who are ready to convert in to paid?
I have data that has actions that perform on my tool and I would like to predict the customers who are ready to convert from free/trail to the paid category.
My data looks like the following:
dummy<-data.frame(license=sample(c("Free","Trail","... |
H: Value of features is zero in Decision tree Classifier
I used CountVectorizer and TfidfVectorizer seperately to vectorize text which is 100K reviews and passed the vector data to a Decision tree Classifier. Upon using _feature_importances__ attribute of Decision tree Classifier, the feature importance values for all... |
H: Architecture for linear regression with variable input where each input is n-sized one-hot encoded
I am relatively new to deep learning (got some experience with CNNs in PyTorch), and I am not sure how to tackle the following idea. I want to parse a sentence, e.g. I like trees., one-hot encoded the parse output of ... |
H: How does a Bayes regularization works?
I'm trying to get grasp of Bayes regularization algorithm.
List of symbols 1st:
$F$ - objective function
$\gamma$ - regularization parameter
$M$ - number od neural network weights
$N$ - number of data tuples
$e$ - modeling error
$w$ - net weight
$D$ - data set input-output pai... |
H: Mean Absolute Error in Random Forest Regression
I am new to the whole ML scene and am trying to resolve the Allstate Kaggle challenge to get a better feeling for the Random Forest Regression technique.
The challenge is evaluated based on the MAE for each row.
I've run the sklearn RandomForrestRegressor on my vali... |
H: What algorithms can be used to derive matching rules between known matches in datasets?
Lets say I have two datasets with different column names except for a unique ID key
Table 1 CSV
first_name,middle_name,last_name,uno,id
John,D,Smith,1,1
John,C,Smith,1,2
John,B,Doe,1,3
Suzy,C,Q,1,4
Table 2 CSV
fname,mname,lname... |
H: What is the error rate for random guessing?
I am studying for my Machine Learning exam. In sample exam questions, there is a specific one that I could not understand:
And the question is: "What is the error rate for random guessing?"
I could not understand the question, can anyone explain it to me?
AI: Random gues... |
H: Why normalization kills my accuracy
I have a binary sound classifier. I have a feature set that is extracted from audio with size of 48. I have a model(multi layer neural network) that has around %90 accuracy on test and validation sets. (without normalization or Standardization)
I see that the feature values are m... |
H: Reinforcement learning for continuous state and action space
Problem
My goal is to apply Reinforcement Learning to predict the next state of an object under a known force in a 3D environment (the approach would be reduced to supervised learning, off-line learning).
Details of my approach
The current state is the ve... |
H: Are there real world applications where deep fully connected networks are better suited than ConvNets
I would like to give some brief background for my question to avoid answers that explain the difference between fully connected nets and ConvNets.
I completed the first 3 courses in the deep learning specialization... |
H: RL - Weighthing negative rewards
Let's consider that I give an agent a reward of -1 (minimum reward) every time it performs an action which leads to the premature end of the episode (i.e., the agent dies). Besides, I also give a negative reward in the [-1, 0) interval when the agent performs an action that I want t... |
H: What are Machine learning model characteristics?
This question I have received in some Machine Learning related interview and Here is the question
What questions would you ask to learn about machine learning model characteristics?
This is what I think:
I did a bit research on the internet & found this resource,... |
H: How to represent linear regression in a decision tree form
I have read that decision trees can represent any hypothesis and are thus completely expressive. So how do we represent the hypothesis of linear regression in the form of a decision tree ? I am referring to the equation
w0 + w1.x1 + w2.x2 + .... + wn.xn = ... |
H: Optimal proportion between the amount of Class = 1 and the amount of Class = 0?
I am quite new machine learning methods, so I may not write proper technical formulas.
My question is about the optimal proportion between sample size in Class = 1 and Class = 0 in a binary classification problem. (I might use different... |
H: How to represent a user who hasn't churned in training data
I am building a file with sample data that has a bunch of variables:
date, customer_id, amount_spent, number_of_transactions, time_since_last_transaction etc. that i am mapping against days_to_churn
I will train my model using Keras to map the emboldened v... |
H: Why does Feature Importance change with each iteration of a Decision Tree Classifier?
After applying PCA to reduce the number of features, I am using a DecisionTreeClassifier for a ML problem
Additionally I want to compute the feature_importances_. However, with each iteration of the DecisionTreeClassifier, the fe... |
H: What is a suitable Tensorflow model to classify images into foggy/not foggy?
I want to classify photos taken by multiple webcams that are operating in mountainous regions into foggy / not foggy. The photos are in various sizes and were taken under very different light conditions and in different areas.
I read about... |
H: Strategies for continuously assessing and improving model performance
I am building a supervised machine learning model to generate forecast.
So I would have historic data like this:
SKU, Month, .... other features, Actual Volume
That I can use a model to generate forecast, using the actual volume as the label.
Of... |
H: Image normalisation methods
I have found some research papers specifying explicitly the normalisation technique they used to get the results.
What difference do
IMG /255.0
And
IMG - mean / STD
Has on the performance of the CNNs?
AI: So, before just diving into the performance of the CNNs based on the two method... |
H: What features are extracted from pre-trained model of CNN Keras?
I would like to use the CNN pre-trained model in feature extraction but I don't know what features are extracted from that.
Please let me know!
AI: Here is a good post about how features are extracted using CNN: https://towardsdatascience.com/cnn-appl... |
H: Data Science Career: From Researcher To Data Scientist
I’m currently working as a Research Assistant in Computer Science, specializing in both Human-Computer Interaction (HCI) and Health Informatics (HI) fields. As part of my role, I collect data from several clinical professionals (clinicians, physicians, and doct... |
H: How to get mean test scores from GridSearchCV with multiple scorers - scikit-learn
I'm trying to get mean test scores from scikit-learn's GridSearchCV with multiple scorers.
grid.cv_results_ displays lots of info. But
grid.cv_results_['mean_test_score'] keeps giving me an error.
I've checked the docs and similar ... |
H: How to pass 2 features to LSTM , one of them is one-hot-encoded with Keras?
I have a very simple LSTM model
model = Sequential()
model.add(LSTM(64, input_shape=(seq_length, X_train.shape[2]) , return_sequences=True))
model.add(Dense(y_cat_train.shape[2], activation='softmax'))
model.compile(loss='categorical_cross... |
H: WEKA Random Forest J48 Attribute Importance
I have been using WEKA to classify very long duration audio recordings. The best performing classifiers have been Random Forest and J48. The attributes used to classify the audio are acoustic indices. This process of generating these indices is quite resource intensive.
... |
H: Using neural network for "features matching" binary classification
We have a dataset of numerical features from two images and we want to check if these images match or not using only these features.
Basically we have have these columns:
fA1, fA2, ..., fA14: 14 features from image A
fB1, fB3, ..., fB14: 14 feature... |
H: How to classify images Neural Network didn't trained to Understand
Let's say I trained a Convolution neural network to Identify Cats , Dogs and wolves . But suddenly I feed it pictures of rabbits and Lions. so how can I classify those as pictures as "Other"
I tried to do this by Adding "Sigmoid" activation functi... |
H: Ploting eigenvectors
I've generated two clouds of 3d points from multivariate_normal
data = np.random.multivariate_normal([2,2,2],[[1,0,0],[0,5,0],[0,0,10]],
size=500)
data = np.vstack((data, np.random.multivariate_normal([-2,-2,-2], [[1,0,0],[0,5,0],[0,0,10]], size=500)))
data = data - data.mean(axis=0)
And try... |
H: Accuracy keep changing by changing randomState of classifier
I try to classify car sound samples. Using MLPClassifier from Scikit. I'm getting very different and confusing test results between 2 different test sets, and I am stuck:
Training is done with the first data set of 1500 samples, splitted as 70/30 train/t... |
H: how to get prediction from trained random forest model?
i have a dataset with two columns user posts (posts) and personality type (type) , i need personality type according to posts using this dataset so i used random forest regression for prediction
here is my code:-
df = pd.read_csv('personality_types.csv')
cou... |
H: Uniformity of color and texture in an image
I am new to the field of deep learning and have a problem in determining whether two images have uniform color and texture. For example, I have a
Master image -
Now, with respect to this image i need to determine whether the following images have uniform texture and col... |
H: Numpy element wise comparison for a particular value in two arrays
If I have two arrays as shown below:
a = numpy.array([0, 0, 1, 0, 1, 1, 1, 0, 1])
b = numpy.array([1, 1, 1, 0, 0, 1, 1, 0, 0])
Is there an easy way using numpy to count the number of occurrences where elements at the same index in each of the two a... |
H: Machine Learning library in Python, list or numpy or pandas
Background: We are trying to build a customized ML library in Python 3 to tackle analysis we often repeat, in a general fashion. But it would not be nearly as general as sklearn. In fact, we are prepared to break some interfaces if that give us enough perf... |
H: Use MinMaxScaler , label encoder, one hot encoder , keras prediction file for later prediction
I'm new to neural networks and data science field. I have a dataset with over 90,000 rows. which Include 9 text columns & 29 Number Columns. after encoding with label encoder and one hot encoder It has over 10,000 columns... |
H: Are RNN or LSTM appropriate Neural Networks approaches for multivariate time-series regression?
Dear Data Science community,
For a small project, I've started working on Neural networks as a regression tool, but I am still confused about possibilities of some variants. Here's what I am aiming to do:
I have multipl... |
H: Is it possible to make a CS:GO Machine Learning AI?
I am not an expert on Machine Learning, Neural Networks or NEAT. In fact, I probably have no clue what I'm talking about. My question is if you can make a learning AI that learns to play complex multiplayer games and possibly outpreform humans. If it is possible, ... |
H: Machine Learning applied to database design
Is it possible to apply machine learning to database design given a data source with (pseudo) relationships?
AI: This is such an interesting question. I suppose that it is possible but you would have to answer some more questions before you can actually get help with mode... |
H: Machine Learning in real time
I am a newbie in ML world, but very curious and enthusiastic about it. Have gone through articles and some hands-on too. Still got a silly doubt.
In sample datasets (like Iris or diabetes or breast cancer etc.) and exercises, I find the data to be well-formed and ready to be digested b... |
H: Scaling label encoded values for Linear Algorithms
I have encoded categorical variables to numerical values. As we know that for feeding values to Linear Algorithms like SVM or KNN, we scale the values for columns having large variations.
I have three label encoded columns, one of which has unique values from 1-3,... |
H: Given a 12x12 binary image (only black and white pixels) what is its dimensionality? And how can I define dimensionality of a data space?
Suppose I have a grid 12x12 of pixels that can be only black or white. I can't understand if the dimensionality is 2 or 3. I mean... Is dimension given by 12x12 or 12x12x2 ?
AI: ... |
H: Difference in labelling and normalizing train/test data
I am working on a dataset comprised of almost 17000 data points. Since it's a financial dataset and the components are many different companies, I need necessarily to split it by date. Therefore, supposing I have 10 years of data, I am training over the first ... |
H: Should I scale my features?
I have a dataset that looks something like this;
ID | Location | Job_title | blue_jumper | red_jumper | yellow_jumper | green_jumper | Target(purple_jumper)
-------------------------------------------------------------------------------------------------------------------
B7372 | Rome... |
H: Why does averaging a sentence's worth of word vectors work?
I am working on a text classification problem using r8-train-all-terms.txt, r8-test-all-terms.txt from https://www.cs.umb.edu/~smimarog/textmining/datasets/. The goal is to predict the label using a Random Forest classifier.
Each text sentence as been ve... |
H: How the original data can be written in the space defined by these M principal components?
Suppose you apply PCA on the data $x_1,...,x_6$ and find that data can be fully described using M principal components $u_1,...,u_M$. How the original data can be written in the space defined by these M principal components?
... |
H: How to use correlation matrix when the dataset contains multiple columns with text data?
How to use it with Amazon fine food reviews dataset?
AI: The problem is that the correlation matrix has to be done with numerical values. So what you have to do is to transform the texts into numerical vectors. There are severa... |
H: How much data to use for feature selection?
Working on my master's thesis, this is a problem I'm unable to find good resources about.
I'm working with data of 18 participants, who are either active or passive. Each participant is then subjected to a 3 x 3 experiment and results in a total of around 676 trials per p... |
H: What is Monte Carlo dropout?
I understand how to use MC dropout from this answer, but I don't understand how MC dropout works, what its purpose is, and how it differs from normal dropout.
AI: Let's start with normal dropout, i.e. dropout only at training time. Here, dropout serves as a regularization to avoid overf... |
H: Trying to find the correlation between inputs and output
I have tried the pandas code for trying to find out the correlation between the output and the inputs I am feeding. Here is the code:
dataframe.corrwith(dataframe['output']).plot(kind='barh',figsize=[20,10], legend=True,grid=True)
I got the following image... |
H: Generated training set on convnet
I have a dataset with roughly 800 images that are classified in 18 classes.
The classes are spread unevenly, with some classes having 30 images and others having 5.
In order to increase my dataset,I've decided to use image augmentation modifying each image a little,making 20 new ... |
H: CNN to many outputs
I have a dataset with 100 columns (categorial one-hot encoded) and 1 column with text data (simple sentences) and i want to build a neural network to arround 380.000 outputs labels. I have no idea what can i do.
I was thinking about a CNN with Embedding Layer and then many dense layers.
Any sug... |
H: How can I know if my NN TensorFlow model is overfitted or not?
I am new with TensorFlow (Python) and I can not juge my obtained results in terms of training and testing accuracy
I am using the GradientDescentOptimizer with a learning coeff equal to 10^(-4) and I have executed the following code :
for gg in range ... |
H: Can parallel computing be utilized for boosting?
Since boosting is sequential, does that mean we cannot use multi-processing or multi-threading to speed it up? If my computer has multiple CPU cores, is there anyway to utilized these extra resources in boosting?
AI: You can estimate in parallel each of the weak lear... |
H: Difference between a target and a label in machine learning
If I have a supervised learning system (for example for the MNIST dataset) I have features (pixel values of MNIST data) and labels (correct digit-value).
However sometimes people use the word target (instead of label).
Are target and label interchangeable?... |
H: Disadvantages of hyperparameter tuning on a random sample of dataset
I often work with very large datasets where it would be impractical to check all relevant combinations of hyperparameters when constructing a machine learning model. I'm considering randomly sampling my dataset and then performing hyperparameter t... |
H: Filtering Pandas Dataframe Results without saving to a variable first
Is there a way to more efficiently filter a result on a data frame without having to explicitly save it in a variable and then filter? For instance, in the code below I would like to add something to line 3 to be able to achieve either df1 or df2... |
H: Loss and Regularization inference
I'm building a Matrix Factorization model for MovieLens dataset with batch-wise training. Loss function for the batch:
$$
L_{batch} = 1/|B|\sum_{(u,i)\in{B}}(r_{ui} - \mu - b_u - b_i - p_u^Tq_i)^2 + \lambda(||p_u||^2 + ||q_i||^2)
$$
$$
L_{batch} = (L_{base\_loss} + L_{reg\_loss})/... |
H: Guidelines for vocabulary sizes for BoW
I am currently trying to get a vocabulary for BoW-vector generation out of a set of 200k scientific abstracts.
I do some basic filtering of tokens already like lowercasing, stop-word-removal, stemming, not taking tokens with size < 2, leaving tokens out that can be converted... |
H: What could explain a much higher F1 score in comparision to accuracy score?
I am building a binary classifier, which classifies numerical data, using Keras.
I have 6992 datapoints in my dataset. Test set is 30% of the data. And validation set is 30% of the training set.
When evaluating the model, I get these valu... |
H: Word embeddings for Information Retrieval - Document search?
What are good ways to find for single sentence (query) the most similiar document (text). I asked myself if word vectors (weighted average of the documents) are suitable to map a single sentence to a whole document?
AI: Doc2Vec is on possible approach. Wi... |
H: What is the difference between regplot and lmplot in seaborn?
Seaborn library in python suggests to use either lmplot or regplot to visualise a regression between two variables. What is the difference between the two plots ? The result I was able to get are slightly different but I have no idea why !
AI: regplot() ... |
H: Why do a lot of people use ipython notebook over python file when doing analyzing data? Is it the same in industry?
I have seen that a lot of people write code in ipython notebook when doing statistical analysis on data, apart from easy visualization after each step rather than running the whole code every time on ... |
H: How to use vectors produced by TF-IDF as an input for fuzzy c-means?
I have done text processing with TF-IDF method and as an output got a list of normalized vectors [0, 1] for each document. Such as below:
Document 1
word1:1.0, word2:0.9, ..., word_n:0
Document 2
word2:1.0, word1:0.4, ..., word_n:0
...
etc
The ... |
H: LSTM for prediction of next location step - help with standardization
I have a few questions regarding the topic and I hope someone might have experience with any of them.
What I am trying to do is train an LSTM network, whose input is a sequence of N steps in a XYZ space (i.e 3 features over N point per sample, ea... |
H: Size of Output vector from AvgW2V Vectorizer is less than Size of Input data
Hi,
I have been seeing this problem for quite some time. Whenever I tried vectorizing input text data though avgw2v vectorization technique. The size of vectorized data is less than the size of the input data. Is there any statistical reas... |
H: What is exactly meant by neural network that can take different types of input?
There is a scientific document that implements a convolutional neural network to classify 3 different types of data, although how exactly, is unknown to me.
Here's the explanation of network architecture:
This section describes archite... |
H: Naive bayes, all of the elements in predict_proba output matrix are less than 0.5
I've created a MultinomialNB classifier model by which I'm trying to label some test texts:
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn import preprocessing
from sklearn.naive_bayes import MultinomialNB
t... |
H: NLP: What are some popular packages for phrase tokenization?
I'm trying to tokenize some sentences into phrases. For instance, given
I think you're cute and I want to know more about you
The tokens can be something like
I think you're cute
and
I want to know more about you
Similarly, given input
Today was gr... |
H: How does dropout work during testing in neural network?
The below paragraph is picked from the textbook Hands-On Machine Learning with sci-kit learn & Tensorflow. I couldn't understand what the author is trying to convey. It would be really grateful if someone can provide an explanation for below paragraph with an ... |
H: Why BatchNormalization fails in Keras
I try to test ResNet approach on cifar10 dataset with the following python code:
# load data
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
X_train = X_train.astype('float32')/256
X_test = X_test.astype('float32')/256
y_train = keras.utils.to_categorical(y_train)
y_... |
H: What is the difference between dynamic programming and Q-learning?
What is the difference between the DP-based algorithm and Q-learning?
AI: Both Q learning and Value Iteration (a DP technique) use similar update rules based on Bellman optimality equations:
$$v_*(s) = \text{max}_{a}\sum_{s',r} p(s',r|s,a)(r + \gamm... |
H: How to get out of local minimums on stochastic gradient descent?
I'm not programming a neural network but I'm looking at it from a non-hands-on, theoretical point of view and I'm currently wondering how to escape a local minimum and how to get to a global minimum.
If you start at a point, for instance: (red)
When ... |
H: Normalization before or after resizing
I'm training deep learning network using images (to be exact - I'm solving semantic segmentation problem).
What's the proper order of resizing (I need to resize images to fixed width X height) and normalization (dividing by 255 value) of images in preprocessing?
Does it make m... |
H: Oversampling before Cross-Validation, is it a problem?
I have a multi-class classification problem to solve which is highly imbalanced. Obviously I'm doing oversampling, but I'm doing cross-validation with the over-sampled dataset, as a result of which I should be having repetition of data in the train as well as v... |
H: How to calculate Accuracy, Precision, Recall and F1 score based on predict_proba matrix?
I found this link that defines Accuracy, Precision, Recall and F1 score as:
Accuracy: the percentage of texts that were predicted with the correct tag.
Precision: the percentage of examples the classifier got right out of the t... |
H: Predicting service date
If I had an automated system that pays my bills, but the website where I pay them won't tell me when the next bill will be available. What is a good approach of predicting the date of the next bill? I don't see a regression model here as a good approach. I believe it's better to get a simple... |
H: Protein interaction prediction- how to input this data structure
I know the basics of machine learning and have quite an experience with time series data or data fed in a tabular format. But in the picture, the data is arranged as a graph. Is there a way to input the graph into a ML tool such as Artificial Neural ... |
H: predict future value in every one hour using (t+60 minutes) LSTM neural network in python
I have a data csv file including with three inputs and two output with time series. Here data took an every one hour one hour. So I need to predict my next future value at t+60 according to the previous input value and at that... |
H: Classification: how to handle reviews/long english words in feature set with all other numerical features
I am currently working on an use case where feature set contains numeric values such as amount, as well as a review feature which contains long winded english text.
the english text will very well differ betwee... |
H: Reg. Pandas factorize()
-Hi Experts-
I just read about factorise() function in Pandas. Using this I'm able to encode (enumerate) my string values into numbers. But, now I'm not able to understand what numbers corresponds to what string.
Ex.
df['product_name'] # Ex. A, B, C
df['product_name'] = df['product_name']... |
H: How to make a region of interest proposal from convolutional feature maps?
Problem
Keras does not have any direct implementation of region of interest pooling. I am aware of how to perform maxpooling, but I don't know how to get bounding boxes from feature maps passed from convolutional layer.
Is there any way to d... |
H: Confusion on Delta Rule and Error
I'm currently reading Mitchell's book for Machine Learning, and he just started gradient descent. There's one part that's really confusing me.
At one point, he gives this equation for the error of a perceptron over a set of training examples.
$$E(\vec{w})\equiv \frac12 \sum_{d \in ... |
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