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K-fold cross validation using Scikit-Learn

35:26

GradientBoostingClassifier using Scikit-Learn

24:58

ExtraTreesClassifier using Scikit-Learn

21:21

Quadratic Discriminant Analysis (QDA) using Scikit-Learn

15:11

CatBoost Classifier | Machine Learning | Python

26:23

Bagging Classifier using Scikit-Learn

18:26

GridSearchCV using Scikit-Learn

𝐆𝐫𝐢𝐝𝐒𝐞𝐚𝐫𝐜𝐡𝐂𝐕 is a function that comes with Scikit-Learn library and it is a process for tuning hyperparameters in machine learning models. The performance of a machine learning model significantly depends on the selection of hyperparameters. 𝐆𝐫𝐢𝐝𝐒𝐞𝐚𝐫𝐜𝐡𝐂𝐕 loops through a predefined set of hyperparameters and selects the optimal values from them after exhaustively considering all parameter combinations.
𝗚𝗶𝘁𝗛𝘂𝗯 𝗮𝗱𝗱𝗿𝗲𝘀𝘀: github.com/randomaccess2023/MG2023/tree/main/Video%2081
𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝘁𝗶𝗺𝗲𝘀𝘁𝗮𝗺𝗽𝘀:
01:09 - Import required libraries
02:48 - Load 𝐞𝐚𝐫𝐥𝐲_𝐬𝐭𝐚𝐠𝐞_𝐝𝐢𝐚𝐛𝐞𝐭𝐞𝐬_𝐫𝐢𝐬𝐤_𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧 dataset
05:40 - Perform preprocessing
07:42 - Separate features and classes
08:35 - Apply 𝐆𝐫𝐢𝐝𝐒𝐞𝐚𝐫𝐜𝐡𝐂𝐕 in 𝐑𝐚𝐧𝐝𝐨𝐦 𝐅𝐨𝐫𝐞𝐬𝐭 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐞𝐫
15:10 - Apply 𝐆𝐫𝐢𝐝𝐒𝐞𝐚𝐫𝐜𝐡𝐂𝐕 in 𝐄𝐱𝐭𝐫𝐚 𝐓𝐫𝐞𝐞𝐬 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐞𝐫
17:59 - Apply 𝐆𝐫𝐢𝐝𝐒𝐞𝐚𝐫𝐜𝐡𝐂𝐕 in 𝐆𝐫𝐚𝐝𝐢𝐞𝐧𝐭 𝐁𝐨𝐨𝐬𝐭𝐢𝐧𝐠 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐞𝐫
20:50 - Apply 𝐆𝐫𝐢𝐝𝐒𝐞𝐚𝐫𝐜𝐡𝐂𝐕 in all the models
#sklearn #scikitlearn #datascience #jupyternotebook #machinelearning #gridsearchcv #hyperparametertuning #python #pythonprogramming

Відео

K-fold cross validation using Scikit-Learn

35:26

K-fold cross validation using Scikit-Learn

Переглядів 5021 годину тому

𝐊-𝐟𝐨𝐥𝐝 𝐜𝐫𝐨𝐬𝐬 𝐯𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧 is a technique used for evaluating the performance of machine learning models. It uses different portions of the dataset as train and test sets in multiple iterations and helps a model to generalize well on unseen data. Scikit-Learn's 𝐭𝐫𝐚𝐢𝐧_𝐭𝐞𝐬𝐭_𝐬𝐩𝐥𝐢𝐭 method uses a fixed set of samples as the train set and the rest of the samples outside the train set as the test set, wh...

GradientBoostingClassifier using Scikit-Learn

24:58

GradientBoostingClassifier using Scikit-Learn

Переглядів 68День тому

𝐆𝐫𝐚𝐝𝐢𝐞𝐧𝐭𝐁𝐨𝐨𝐬𝐭𝐢𝐧𝐠𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐞𝐫 is a supervised machine learning algorithm. It builds an additive model in a forward stage-wise fashion and allows for the optimization of arbitrary differentiable loss functions. 𝗚𝗶𝘁𝗛𝘂𝗯 𝗮𝗱𝗱𝗿𝗲𝘀𝘀: github.com/randomaccess2023/MG2023/tree/main/Video 79 𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝘁𝗶𝗺𝗲𝘀𝘁𝗮𝗺𝗽𝘀: 00:47 - Import required libraries 02:24 - Load 𝐜𝐫𝐞𝐝𝐢𝐭_𝐚𝐩𝐩𝐫𝐨𝐯𝐚𝐥 dataset 04:38 - Perform preprocessi...

21:21

ExtraTreesClassifier using Scikit-Learn

Переглядів 7914 днів тому

𝐄𝐱𝐭𝐫𝐚𝐓𝐫𝐞𝐞𝐬𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐞𝐫 is a supervised machine learning algorithm. It is a type of ensemble learning technique which fits a number of randomized decision trees (i.e., extra trees) on various sub-samples of the dataset. It contributes to reducing the variance of the model and results in less overfitting. 𝗚𝗶𝘁𝗛𝘂𝗯 𝗮𝗱𝗱𝗿𝗲𝘀𝘀: github.com/randomaccess2023/MG2023/tree/main/Video 78 𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝘁𝗶𝗺𝗲𝘀𝘁𝗮𝗺𝗽𝘀: 01...

Quadratic Discriminant Analysis (QDA) using Scikit-Learn

15:11

Quadratic Discriminant Analysis (QDA) using Scikit-Learn

Переглядів 6521 день тому

𝐐𝐮𝐚𝐝𝐫𝐚𝐭𝐢𝐜 𝐃𝐢𝐬𝐜𝐫𝐢𝐦𝐢𝐧𝐚𝐧𝐭 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 (𝐐𝐃𝐀) is a supervised machine learning algorithm. It is very similar to Linear Discriminant Analysis (LDA) except the assumption that the classes share the same covariance matrix. In other words, each class has its own covariance matrix. In this case, the boundary between classes is a quadratic surface instead of a hyperplane. 𝗚𝗶𝘁𝗛𝘂𝗯 𝗮𝗱𝗱𝗿𝗲𝘀𝘀: github.com/randomacc...

CatBoost Classifier | Machine Learning | Python

26:23

CatBoost Classifier | Machine Learning | Python

Переглядів 137Місяць тому

Categorical Boosting (𝐂𝐚𝐭𝐁𝐨𝐨𝐬𝐭) is a gradient-boosting algorithm for machine learning. Gradient boosting is a process in which many decision trees are constructed iteratively. In CatBoost, each successive tree is built with reduced loss compared to the previous trees. I used 𝐦𝐮𝐬𝐡𝐫𝐨𝐨𝐦𝐬.𝐜𝐬𝐯 dataset for this example. The dataset is available in the repository. It contains 2 types of mushrooms in t...

18:26

Bagging Classifier using Scikit-Learn

Переглядів 38Місяць тому

𝐁𝐚𝐠𝐠𝐢𝐧𝐠 is a supervised machine learning algorithm. It is an ensemble learning technique in which multiple base estimators are trained independently and in parallel on different subsets of the training data. The final prediction is made by aggregating all the predictions of the base estimators. I used 𝗴𝗹𝗮𝘀𝘀_𝗶𝗱𝗲𝗻𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻.𝗰𝘀𝘃 dataset in this example. The dataset is available in the repository. ...

Artificial neural network for regression task using PyTorch

28:01

Artificial neural network for regression task using PyTorch

Переглядів 106Місяць тому

A regression analysis in machine learning is used to investigate the relationship between one or more independent variables (treated as 𝘧𝘦𝘢𝘵𝘶𝘳𝘦𝘴) and a dependent variable (regarded as 𝘰𝘶𝘵𝘤𝘰𝘮𝘦). It is a method for predictive modelling and is used to predict a continuous outcome. I used 𝘴𝘬𝘭𝘦𝘢𝘳𝘯'𝘴 𝐜𝐚𝐥𝐢𝐟𝐨𝐫𝐧𝐢𝐚 𝐡𝐨𝐮𝐬𝐢𝐧𝐠 dataset for this example. This dataset has 8 features and I built a very simple ar...

19:22

Hartigan index using Python

Переглядів 11Місяць тому

𝐇𝐚𝐫𝐭𝐢𝐠𝐚𝐧 𝐢𝐧𝐝𝐞𝐱 (𝐇𝐈) is computed by taking the logarithm of the ratio among the sum-of-squares between each cluster (𝐒𝐒𝐁) and the sum-of-squares within the clusters (𝐒𝐒𝐖). It is a cluster evaluation technique. 𝗚𝗶𝘁𝗛𝘂𝗯 𝗮𝗱𝗱𝗿𝗲𝘀𝘀: github.com/randomaccess2023/MG2023/tree/main/Video 73 𝙄𝙢𝙥𝙤𝙧𝙩𝙖𝙣𝙩 𝙩𝙞𝙢𝙚𝙨𝙩𝙖𝙢𝙥𝙨: 00:57 - Import required libraries 04:03 - Create data 05:07 - Perform preprocessing 05:19 - Perf...

Linear Discriminant Analysis using Scikit-Learn

19:41

Linear Discriminant Analysis using Scikit-Learn

Переглядів 43Місяць тому

𝐋𝐢𝐧𝐞𝐚𝐫 𝐃𝐢𝐬𝐜𝐫𝐢𝐦𝐢𝐧𝐚𝐧𝐭 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 (𝐋𝐃𝐀) is a supervised machine learning algorithm. This approach is used in machine learning to solve classification problems with two or more classes. 𝐋𝐃𝐀 fits a Gaussian density to each class, assuming all classes share the same covariance matrix. I used 𝗿𝗮𝗶𝘀𝗶𝗻.𝘅𝗹𝘀𝘅 dataset for this example. The dataset is available in the repository. It contains 2 types of raisins...

XGBoost Classifier | Machine Learning | Python API

15:41

XGBoost Classifier | Machine Learning | Python API

Переглядів 512 місяці тому

eXtreme Gradient Boosting (𝐗𝐆𝐁𝐨𝐨𝐬𝐭) is a gradient-boosting algorithm for machine learning. 𝐗𝐆𝐁𝐨𝐨𝐬𝐭 builds a predictive model by combining the predictions of multiple individual models, often decision trees, in an iterative manner. I used 𝗯𝗮𝗻𝗸𝗻𝗼𝘁𝗲_𝗮𝘂𝘁𝗵𝗲𝗻𝘁𝗶𝗰𝗮𝘁𝗶𝗼𝗻.𝗰𝘀𝘃 dataset for this example. The dataset is available in the repository. It contains 2 types of entities in the target column: 𝟬 & 𝟭. ...

LightGBM Classifier | Machine Learning | Python API

20:22

LightGBM Classifier | Machine Learning | Python API

Переглядів 792 місяці тому

Light Gradient-Boosting Machine (𝐋𝐢𝐠𝐡𝐭𝐆𝐁𝐌) is a gradient-boosting algorithm for machine learning. It uses a histogram-based method in which data is bucketed into bins using a histogram of the distribution. I used 𝗺𝗮𝗴𝗶𝗰_𝗴𝗮𝗺𝗺𝗮_𝘁𝗲𝗹𝗲𝘀𝗰𝗼𝗽𝗲.𝗰𝘀𝘃 dataset for this example. The dataset is available in the repository. It contains 2 types of entities in the target column: 𝗴=𝗴𝗮𝗺𝗺𝗮(𝘀𝗶𝗴𝗻𝗮𝗹) & 𝗵=𝗵𝗮𝗱𝗿𝗼𝗻(𝗯𝗮𝗰𝗸𝗴𝗿𝗼...

21:53

AdaBoost Classifier using Scikit-Learn

Переглядів 5022 місяці тому

𝐀𝐝𝐚𝐁𝐨𝐨𝐬𝐭 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐞𝐫 is a supervised machine learning algorithm. AdaBoost is short for 𝐀𝐝𝐚𝐩𝐭𝐢𝐯𝐞 𝐁𝐨𝐨𝐬𝐭𝐢𝐧𝐠 and is used as an ensemble method in machine learning. The core principle of AdaBoost is to fit a sequence of weak learners (i.e., models that are only slightly better than random guessing, such as small decision trees) on repeatedly modified versions of the data. I used 𝗱𝗿𝘆_𝗯𝗲𝗮𝗻.𝘅𝗹𝘀𝘅 dataset...

17:19

Logistic Regression using Scikit-Learn

Переглядів 3422 місяці тому

𝐋𝐨𝐠𝐢𝐬𝐭𝐢𝐜 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧 is a supervised machine learning algorithm. Despite the name, it can be used for a classification task. In this model, the probabilities describing the possible outcomes of a single trial are modeled using a 𝘭𝘰𝘨𝘪𝘴𝘵𝘪𝘤 𝘧𝘶𝘯𝘤𝘵𝘪𝘰𝘯. I used 𝗿𝗶𝗰𝗲.𝗰𝘀𝘃 dataset for this example. The dataset is available in the repository. It contains 2 types of Turkish rice: 𝗖𝗮𝗺𝗺𝗲𝗼 & 𝗢𝘀𝗺𝗮𝗻𝗰𝗶𝗸. 𝑮𝒊𝒕𝑯𝒖𝒃 𝒂...

Complement Naive Bayes using Scikit-Learn

22:10

Complement Naive Bayes using Scikit-Learn

Переглядів 362 місяці тому

𝐂𝐨𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭 𝐍𝐚𝐢𝐯𝐞 𝐁𝐚𝐲𝐞𝐬 is a supervised machine learning algorithm which has been used for a classification task in this example. This algorithm is a modification of 𝐌𝐮𝐭𝐢𝐧𝐨𝐦𝐢𝐚𝐥 𝐍𝐚𝐢𝐯𝐞 𝐁𝐚𝐲𝐞𝐬 and it works well in the case of unbalanced datasets. I used 𝗵𝗮𝗺_𝘀𝗽𝗮𝗺.𝗰𝘀𝘃 dataset for this example. The dataset is available in the repository. It contains 2 types of emails: 𝐡𝐚𝐦 & 𝐬𝐩𝐚𝐦. 𝑮𝒊𝒕𝑯𝒖𝒃 𝒂𝒅𝒅𝒓𝒆𝒔𝒔: github...