Topic: cross-validation-score Goto Github
Some thing interesting about cross-validation-score
Some thing interesting about cross-validation-score
cross-validation-score,Project compares three regression models for predicting the amount of gold recovered from gold ore in order to optimize gold production and eliminate unprofitable parameters. Data provided by Zyfra.
User: adkwn1
cross-validation-score,Model to predict the amount of gold extracted from gold mineral.
User: angelicavelez
cross-validation-score,In this classification i got up with best model for the iris flower data set and i used to code and made my own classification model. tried to visualize dataset and perform train test split as test data and train data by this i came up on Understanding the Evaluation metrics and accuracy score which given Understanding the hands on implementation of AI concepts theoretically learned.
User: balajisubrahmanyam12
cross-validation-score,Built Random Forest classifier from scratch on top of Scikit Learn decision trees. Using Scikit Learn to create data cleaning pipelines, perform grid searches for hyper parameter tuning, and decision tree modeling
User: bhulston
cross-validation-score,Iris dataset
User: ccmkaaa
cross-validation-score,This is a Kaggle Dataset where we classify the cars using their various features. Here I used plotly to visualize the Accuracy Scores. Also I used CrossValScore to get More accurate Accuracy Score.
User: datarohit
Home Page: https://www.kaggle.com/datarohitingole/multiple-classification-models-data-evaluation
cross-validation-score,Study Project for Yandex Practicum
User: deleusis
cross-validation-score,Exploring a music dataset by examining correlations between numerical variables, running a principal component analysis for dimensionality reduction and finally fitting both scikit learn Decision Tree Classification and Logistic Regression models to compare their performance.
User: femtonelson
cross-validation-score,This folder contains project assignments that solve the problem of a case based on a dataset with hypothesis testing, Supervised and Unsupervised material.
User: hillidatulilmi
cross-validation-score,GridSearchCV, RandomSearchCV For Model optimization and Saving/Loading the model
User: hohasby
cross-validation-score,GridSearchCV For Model optimization
User: hohasby
cross-validation-score,Overcoming overfitting and underfitting
User: hohasby
cross-validation-score,Titanic Survivor Analysis and Prediction
User: iamkirankumaryadav
cross-validation-score,Create a prototype for a machine learning model to predict the amount of gold recovered from gold ore.
User: jodiambra
Home Page: https://jodiambra.github.io/Zyfra-Gold-Recovery-Predictions/
cross-validation-score,Using scikit-learn RandomizedSearchCV and cross_val_score for ML Nested Cross Validation
User: lacerdash
cross-validation-score,Calculate the bias of k-fold cross-validation with hyper-parameter configuration
Organization: ml-opt
cross-validation-score,pipelines chains together multiple steps so that the output of each step is used as input to the next step
User: nani757
cross-validation-score,A model which can predict if the customer will pay the loan or not.
User: prateekagr21
cross-validation-score,Different types of supervised learning models used for classification problem. Included cross validation for finding hyperparameters whenever necessaruy.
User: praveen2812git
cross-validation-score,Prediction Model, Bias and Uncertainty
User: psanghal
cross-validation-score,In this project, I have developed a Machine Learning model to predict whether users will click on ads. By analyzing various characteristics of users who click on ads, we can gain valuable insights and optimize ad campaigns for better engagement.
User: rahulg-101
cross-validation-score,Using various supervised learning estimators in Sci-Kit Learn to get the best prediction accuracy if possible for the pima indians dataset.
User: shuyib
cross-validation-score,K Nearest Neighbours in Python
User: vaitybharati
cross-validation-score,Model-Validation-Methods
User: vaitybharati
cross-validation-score,Machine learning model which can predict the strength of a mixture for given composition of ingredients like cement, slag, ash, water, superplastic, coarse aggregates, fine aggregates, age.
User: varun-n-m
cross-validation-score,
User: zauverer
cross-validation-score,
User: zauverer
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