In this tutorial, you’ll build a deep learning model that will predict the probability of an employee leaving a company. Retaining the best employees is an important factor for most organizations. To build your model, you’ll usethis dataset available at Kaggle, whic...
Learn what are machine learning models, the different types of models, and how to build and use them. Get images of machine learning models with applications.
I am building a quantile regression model using scikit-learn's GradientBoostingRegressor algorithm. I was going to use GridSearchCV for hyperparameter optimization. Two questions: Does it make sense to use gridsearchcv given that I am looking at quantile regression rather than...
To accomplish this objective, loss rates have been generated using Ordinary Least Squares (OLS) stepwise regression run on what are locally dubbed the 'binary files'. The purpose of this paper is to report a front-end processor to OLS which has reduced computer run time by 85 percent for ...
Since we need to classify customers as either churn or no-churn, we’ll train a simple-yet-powerful classification model. Our model uses logistic regression on a telecom company’s historical customer dataset. This set tracks customer demographics, tenure, monthly charges, and m...
A mlp model with 2 hidden layers (each has 30 and 20 hidden units) can be specified below:# Build the model mlp_mod <- mlp(hidden_units = c(30, 20), penalty = tune()) %>% set_engine("brulee", importance = "permutation") %>% set_mode("regression") I ...
hi@BramVanroy, I am relatively new to 🤗transformers. I would like to know is it possible to use a fine-tuned model to be retrained/reused on a different set of labels? The new set of labels may be a subset of the old labels or the old labels + some additional labels. I already...
How to build and evaluate a Decision Tree model for classification using PySpark's MLlib library. Decision Trees are widely used for solving classification problems due to their simplicity, interpretability, and ease of use
We ran a complicated experiment and are struggling to build a linear model that estimates everything we're interested in. We showed each person a description of a product (for illustration, let's say a car) and varied three things: Whether the description was displayed in co...
In XGBoost Regression to predict prices, How to get coefficients, intercepts of model? How to get summary of model like we get in Statsmodel for Linear regression? See below code from xgboost import XGBRegressor # fit model no training data ...