Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/labex-labs/sklearn-practice-plus
[scikit-learn Practice Plus]-In this course, You will practice more labs of scikit-learn. This will help you to master the skills more deeply.
https://github.com/labex-labs/sklearn-practice-plus
List: sklearn-practice-plus
awesome awesome-list challenges course education hands-on labex labs machine-learning pandas programming python scikit-learn
Last synced: 5 days ago
JSON representation
[scikit-learn Practice Plus]-In this course, You will practice more labs of scikit-learn. This will help you to master the skills more deeply.
- Host: GitHub
- URL: https://github.com/labex-labs/sklearn-practice-plus
- Owner: labex-labs
- Created: 2024-05-22T07:27:06.000Z (6 months ago)
- Default Branch: master
- Last Pushed: 2024-05-23T02:44:22.000Z (6 months ago)
- Last Synced: 2024-05-23T08:43:39.628Z (6 months ago)
- Topics: awesome, awesome-list, challenges, course, education, hands-on, labex, labs, machine-learning, pandas, programming, python, scikit-learn
- Homepage: https://labex.io/courses/sklearn-practice-plus
- Size: 30.3 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# scikit-learn Practice Plus
In this course, You will practice more labs of scikit-learn. This will help you to master the skills more deeply.
## Scenarios
| Index | Name | Difficulty | Practice |
|---------|---------------------------------------------------------|--------------|----------------------------------------------------------------------------|
| 001 | 🎯 Mastering Decision Trees | ★☆☆ | Start Challenge |
| 002 | 🎯 Understanding Validation Curves | ★☆☆ | Start Challenge |
| 003 | 🎯 Clustering and Insights | ★☆☆ | Start Challenge |
| 004 | 🎯 Mastering naive bayes | ★☆☆ | Start Challenge |
| 005 | 🎯 Mastering Linear Regression | ★☆☆ | Start Challenge |
| 006 | 🎯 Predicting Flower Types with Nearest Neighbors | ★☆☆ | Start Challenge |
| 007 | 🎯 Understanding Metrics and Scoring | ★☆☆ | Start Challenge |
| 008 | 📖 Manifold Learning on Spherical Data | ★☆☆ | Start Lab |
| 009 | 📖 Faces Dataset Decompositions | ★☆☆ | Start Lab |
| 010 | 📖 Random Classification Dataset Plotting | ★☆☆ | Start Lab |
| 011 | 📖 Multilabel Dataset Generation with Scikit-Learn | ★☆☆ | Start Lab |
| 012 | 📖 Swiss Roll and Swiss-Hole Reduction | ★☆☆ | Start Lab |
| 013 | 📖 Scikit-Learn Libsvm GUI | ★☆☆ | Start Lab |
| 014 | 📖 Vector Quantization With KBinsDiscretizer | ★☆☆ | Start Lab |
| 015 | 📖 Hierarchical Clustering With Scikit-Learn | ★☆☆ | Start Lab |
| 016 | 📖 Transforming the Prediction Target | ★☆☆ | Start Lab |
| 017 | 📖 Feature Agglomeration for High-Dimensional Data | ★☆☆ | Start Lab |
| 018 | 📖 Species Distribution Modeling | ★☆☆ | Start Lab |
| 019 | 📖 Feature Extraction with Scikit-Learn | ★☆☆ | Start Lab |
| 020 | 📖 Comparison of F-Test and Mutual Information | ★☆☆ | Start Lab |
| 021 | 📖 Data Scaling and Transformation | ★☆☆ | Start Lab |
| 022 | 📖 Curve Fitting With Bayesian Ridge Regression | ★☆☆ | Start Lab |
| 023 | 📖 Demonstrating KBinsDiscretizer Strategies | ★☆☆ | Start Lab |
| 024 | 📖 Lasso and Elastic Net | ★☆☆ | Start Lab |
| 025 | 📖 Logistic Regression Model | ★☆☆ | Start Lab |
| 026 | 📖 Joint Feature Selection With Multi-Task Lasso | ★☆☆ | Start Lab |
| 027 | 📖 Applying Regularization Techniques with SGD | ★☆☆ | Start Lab |
| 028 | 📖 Theil-Sen Regression with Python Scikit-Learn | ★☆☆ | Start Lab |
| 029 | 📖 Compressive Sensing Image Reconstruction | ★☆☆ | Start Lab |
| 030 | 📖 FeatureHasher and DictVectorizer Comparison | ★☆☆ | Start Lab |
| 031 | 📖 Decision Tree Regression | ★☆☆ | Start Lab |
| 032 | 📖 Multi-Output Decision Tree Regression | ★☆☆ | Start Lab |
| 033 | 📖 Precompute Gram Matrix for ElasticNet | ★☆☆ | Start Lab |
| 034 | 📖 Plot Huber vs Ridge | ★☆☆ | Start Lab |
| 035 | 📖 Simple 1D Kernel Density Estimation | ★☆☆ | Start Lab |
| 036 | 📖 Scikit-Learn Lasso Regression | ★☆☆ | Start Lab |
| 037 | 📖 Local Outlier Factor for Novelty Detection | ★☆☆ | Start Lab |
| 038 | 📖 Outlier Detection With LOF | ★☆☆ | Start Lab |
| 039 | 📖 Sparse Signal Recovery With Orthogonal Matching Pu... | ★☆☆ | Start Lab |
| 040 | 📖 Plot SGD Separating Hyperplane | ★☆☆ | Start Lab |
| 041 | 📖 Density Estimation Using Kernel Density | ★☆☆ | Start Lab |
| 042 | 📖 Exploring K-Means Clustering with Python | ★☆☆ | Start Lab |
| 043 | 📖 Agglomerative Clustering on Digits Dataset | ★☆☆ | Start Lab |
| 044 | 📖 Step-by-Step Logistic Regression | ★☆☆ | Start Lab |
| 045 | 📖 OPTICS Clustering Algorithm | ★☆☆ | Start Lab |
| 046 | 📖 Biclustering in Scikit-Learn | ★☆☆ | Start Lab |
| 047 | 📖 Empirical Evaluation of K-Means Initialization | ★☆☆ | Start Lab |
| 048 | 📖 Regularization Path of L1- Logistic Regression | ★☆☆ | Start Lab |
| 049 | 📖 Neighborhood Components Analysis | ★☆☆ | Start Lab |
| 050 | 📖 Kernel Density Estimate of Species Distributions | ★☆☆ | Start Lab |
| 051 | 📖 Support Vector Regression | ★☆☆ | Start Lab |
| 052 | 📖 Affinity Propagation Clustering | ★☆☆ | Start Lab |
| 053 | 📖 Hierarchical Clustering Dendrogram | ★☆☆ | Start Lab |
| 054 | 📖 Comparing BIRCH and MiniBatchKMeans | ★☆☆ | Start Lab |
| 055 | 📖 Bisecting K-Means and Regular K-Means Performance ... | ★☆☆ | Start Lab |
| 056 | 📖 Comparing Clustering Algorithms | ★☆☆ | Start Lab |
| 057 | 📖 Demo of HDBSCAN Clustering Algorithm | ★☆☆ | Start Lab |
| 058 | 📖 Mean-Shift Clustering Algorithm | ★☆☆ | Start Lab |
| 059 | 📖 Neural Network Models | ★☆☆ | Start Lab |
| 060 | 📖 Unsupervised Clustering with K-Means | ★☆☆ | Start Lab |
| 061 | 📖 Random Forest OOB Error Estimation | ★☆☆ | Start Lab |
| 062 | 📖 Pixel Importances With Parallel Forest of Trees | ★☆☆ | Start Lab |
| 063 | 📖 Gaussian Process Classification on Iris Dataset | ★☆☆ | Start Lab |
| 064 | 📖 Gaussian Process Classification | ★☆☆ | Start Lab |
| 065 | 📖 Gaussian Process Classification on XOR Dataset | ★☆☆ | Start Lab |
| 066 | 📖 Gaussian Process Regression | ★☆☆ | Start Lab |
| 067 | 📖 Gaussian Process Regression | ★☆☆ | Start Lab |
| 068 | 📖 Gaussian Process Regression: Kernels | ★☆☆ | Start Lab |
| 069 | 📖 Image Segmentation With Hierarchical Clustering | ★☆☆ | Start Lab |
| 070 | 📖 Color Quantization Using K-Means | ★☆☆ | Start Lab |
| 071 | 📖 Plot Dict Face Patches | ★☆☆ | Start Lab |
| 072 | 📖 Gaussian Processes on Discrete Data Structures | ★☆☆ | Start Lab |
| 073 | 📖 Spectral Clustering for Image Segmentation | ★☆☆ | Start Lab |
| 074 | 📖 Model-Based and Sequential Feature Selection | ★☆☆ | Start Lab |
| 075 | 📖 SVM Tie Breaking | ★☆☆ | Start Lab |
| 076 | 📖 Cross-Validation on Digits Dataset | ★☆☆ | Start Lab |
| 077 | 📖 Early Stopping of Gradient Boosting | ★☆☆ | Start Lab |
| 078 | 📖 Machine Learning Cross-Validation with Python | ★☆☆ | Start Lab |
| 079 | 📖 Plot GPR Co2 | ★☆☆ | Start Lab |
| 080 | 📖 Linear Regression Example | ★☆☆ | Start Lab |
| 081 | 📖 Pairwise Metrics and Kernels in Scikit-Learn | ★☆☆ | Start Lab |
| 082 | 📖 Compare Cross Decomposition Methods | ★☆☆ | Start Lab |
| 083 | 📖 Discretizing Continuous Features With KBinsDiscret... | ★☆☆ | Start Lab |
| 084 | 📖 Boosted Decision Tree Regression | ★☆☆ | Start Lab |
| 085 | 📖 Bias-Variance Decomposition With Bagging | ★☆☆ | Start Lab |
| 086 | 📖 Scikit-Learn Elastic-Net Regression Model | ★☆☆ | Start Lab |
| 087 | 📖 Plot Agglomerative Clustering | ★☆☆ | Start Lab |
| 088 | 📖 Map Data to a Normal Distribution | ★☆☆ | Start Lab |
| 089 | 📖 Nearest Neighbors Classification | ★☆☆ | Start Lab |
| 090 | 📖 SVM Classification Using Custom Kernel | ★☆☆ | Start Lab |
| 091 | 📖 SVM Classifier on Iris Dataset | ★☆☆ | Start Lab |
| 092 | 📖 Recursive Feature Elimination | ★☆☆ | Start Lab |
| 093 | 📖 Diabetes Prediction Using Voting Regressor | ★☆☆ | Start Lab |
| 094 | 📖 Plot Forest Iris | ★☆☆ | Start Lab |
| 095 | 📖 Cross-Validation With Linear Models | ★☆☆ | Start Lab |
| 096 | 📖 Text Classification Using Out-of-Core Learning | ★☆☆ | Start Lab |
| 097 | 📖 Hierarchical Clustering With Connectivity Constrai... | ★☆☆ | Start Lab |
| 098 | 📖 Imputation of Missing Values | ★☆☆ | Start Lab |
| 099 | 📖 SVM: Maximum Margin Separating Hyperplane | ★☆☆ | Start Lab |
| 100 | 📖 SVM for Unbalanced Classes | ★☆☆ | Start Lab |
| 101 | 📖 Kernel Approximation Techniques in Scikit-Learn | ★☆☆ | Start Lab |
| 102 | 📖 Blind Source Separation | ★☆☆ | Start Lab |
| 103 | 📖 Independent Component Analysis with FastICA and PC... | ★☆☆ | Start Lab |
| 104 | 📖 Iris Flower Classification with Scikit-learn | ★☆☆ | Start Lab |
| 105 | 📖 Principal Components Analysis | ★☆☆ | Start Lab |
| 106 | 📖 Hyperparameter Optimization: Randomized Search vs ... | ★☆☆ | Start Lab |
| 107 | 📖 Sparse Coding With Precomputed Dictionary | ★☆☆ | Start Lab |
| 108 | 📖 Wikipedia PageRank With Randomized SVD | ★☆☆ | Start Lab |
| 109 | 📖 Decomposing Signals in Components | ★☆☆ | Start Lab |
| 110 | 📖 Validation Curves: Plotting Scores to Evaluate Mod... | ★☆☆ | Start Lab |
| 111 | 📖 Post Pruning Decision Trees | ★☆☆ | Start Lab |
| 112 | 📖 Comparison of Covariance Estimators | ★☆☆ | Start Lab |
| 113 | 📖 Robust Covariance Estimation and Mahalanobis Dista... | ★☆☆ | Start Lab |
| 114 | 📖 Ridge Regression for Linear Modeling | ★☆☆ | Start Lab |
| 115 | 📖 Robust Covariance Estimation in Python | ★☆☆ | Start Lab |
| 116 | 📖 Comparing Online Solvers for Handwritten Digit Cla... | ★☆☆ | Start Lab |
| 117 | 📖 Decision Tree Analysis | ★☆☆ | Start Lab |
| 118 | 📖 Class Probabilities With VotingClassifier | ★☆☆ | Start Lab |
| 119 | 📖 Covariance Matrix Estimation with Scikit-Learn | ★☆☆ | Start Lab |
| 120 | 📖 Preprocessing Techniques in Scikit-Learn | ★☆☆ | Start Lab |
| 121 | 📖 Agglomerative Clustering Metrics | ★☆☆ | Start Lab |
| 122 | 📖 Logistic Regression Classifier on Iris Dataset | ★☆☆ | Start Lab |
| 123 | 📖 Scikit-Learn Multi-Class SGD Classifier | ★☆☆ | Start Lab |
| 124 | 📖 Manifold Learning with Scikit-Learn | ★☆☆ | Start Lab |
| 125 | 📖 Comparing Linear Bayesian Regressors | ★☆☆ | Start Lab |
| 126 | 📖 Incremental Principal Component Analysis on Iris D... | ★☆☆ | Start Lab |
| 127 | 📖 Lasso Model Selection | ★☆☆ | Start Lab |
| 128 | 📖 Model Selection for Lasso Regression | ★☆☆ | Start Lab |
| 129 | 📖 Linear and Quadratic Discriminant Analysis | ★☆☆ | Start Lab |
| 130 | 📖 Plot Concentration Prior | ★☆☆ | Start Lab |
| 131 | 📖 Sparse Inverse Covariance Estimation | ★☆☆ | Start Lab |
| 132 | 📖 Gaussian Mixture Models | ★☆☆ | Start Lab |
| 133 | 📖 Plot Forest Hist Grad Boosting Comparison | ★☆☆ | Start Lab |
| 134 | 📖 Clustering Analysis With Silhouette Method | ★☆☆ | Start Lab |
| 135 | 📖 Plot Multinomial and One-vs-Rest Logistic Regressi... | ★☆☆ | Start Lab |
| 136 | 📖 Comparing K-Means and MiniBatchKMeans | ★☆☆ | Start Lab |
| 137 | 📖 Nearest Centroid Classification | ★☆☆ | Start Lab |
| 138 | 📖 Spectral Biclustering Algorithm | ★☆☆ | Start Lab |
| 139 | 📖 Spectral Co-Clustering Algorithm | ★☆☆ | Start Lab |
| 140 | 📖 Permutation Feature Importance | ★☆☆ | Start Lab |
| 141 | 📖 Probabilistic Predictions With Gaussian Process Cl... | ★☆☆ | Start Lab |
| 142 | 📖 Decision Trees on Iris Dataset | ★☆☆ | Start Lab |
| 143 | 📖 Nested Cross-Validation for Model Selection | ★☆☆ | Start Lab |
| 144 | 📖 Permutation Test Score for Classification | ★☆☆ | Start Lab |
| 145 | 📖 Recursive Feature Elimination With Cross-Validatio... | ★☆☆ | Start Lab |
| 146 | 📖 MNIST Multinomial Logistic Regression | ★☆☆ | Start Lab |
| 147 | 📖 Scaling Regularization Parameter for SVMs | ★☆☆ | Start Lab |
| 148 | 📖 Plotting Validation Curves | ★☆☆ | Start Lab |
| 149 | 📖 Isotonic Regression with Scikit-Learn | ★☆☆ | Start Lab |
| 150 | 📖 Tuning Hyperparameters of an Estimator | ★☆☆ | Start Lab |
| 151 | 📖 Digits Classification using Scikit-Learn | ★☆☆ | Start Lab |
| 152 | 📖 Gradient Boosting Monotonic Constraints | ★☆☆ | Start Lab |
| 153 | 📖 Revealing Iris Dataset Structure via Factor Analys... | ★☆☆ | Start Lab |
| 154 | 📖 Feature Selection with Scikit-Learn | ★☆☆ | Start Lab |
| 155 | 📖 Scikit-Learn Confusion Matrix | ★☆☆ | Start Lab |
| 156 | 📖 Recognizing Hand-Written Digits | ★☆☆ | Start Lab |
| 157 | 📖 Gradient Boosting Regularization | ★☆☆ | Start Lab |
| 158 | 📖 Plot Topics Extraction With NMF Lda | ★☆☆ | Start Lab |
| 159 | 📖 DBSCAN Clustering Algorithm | ★☆☆ | Start Lab |
| 160 | 📖 Gaussian Mixture Model Initialization Methods | ★☆☆ | Start Lab |
| 161 | 📖 Active Learning Withel Propagation | ★☆☆ | Start Lab |
| 162 | 📖 Document Biclustering Using Spectral Co-Clustering... | ★☆☆ | Start Lab |
| 163 | 📖 Partial Dependence and Individual Conditional Expe... | ★☆☆ | Start Lab |
| 164 | 📖 ROC With Cross Validation | ★☆☆ | Start Lab |
| 165 | 📖 Label Propagation Learning | ★☆☆ | Start Lab |
| 166 | 📖 Ensemble Methods Exploration with Scikit-Learn | ★☆☆ | Start Lab |
| 167 | 📖 Multi-Class AdaBoosted Decision Trees | ★☆☆ | Start Lab |
| 168 | 📖 Isotonic Regression with Scikit-Learn | ★☆☆ | Start Lab |
| 169 | 📖 Sparse Signal Regression With L1-Based Models | ★☆☆ | Start Lab |
| 170 | 📖 Plotting Learning Curves | ★☆☆ | Start Lab |
| 171 | 📖 Non-Negative Least Squares Regression | ★☆☆ | Start Lab |
| 172 | 📖 Quantile Regression with Scikit-Learn | ★☆☆ | Start Lab |
| 173 | 📖 Semi-Supervised Learning Algorithms | ★☆☆ | Start Lab |
| 174 | 📖 Outlier Detection with Scikit-Learn | ★☆☆ | Start Lab |
| 175 | 📖 Categorical Data Transformation using TargetEncode... | ★☆☆ | Start Lab |
| 176 | 📖 Underfitting and Overfitting | ★☆☆ | Start Lab |
| 177 | 📖 AdaBoost Decision Stump Classification | ★☆☆ | Start Lab |
| 178 | 📖 Shrinkage Covariance Estimation | ★☆☆ | Start Lab |
| 179 | 📖 Plotting Predictions With Cross-Validation | ★☆☆ | Start Lab |
| 180 | 📖 Exploring K-Means Clustering Assumptions | ★☆☆ | Start Lab |
| 181 | 📖 Visualize High-Dimensional Data with MDS | ★☆☆ | Start Lab |
| 182 | 📖 Robust Linear Estimator Fitting | ★☆☆ | Start Lab |
| 183 | 📖 Evaluating Machine Learning Model Quality | ★☆☆ | Start Lab |
| 184 | 📖 Caching Nearest Neighbors | ★☆☆ | Start Lab |
| 185 | 📖 Optimizing Model Hyperparameters with GridSearchCV | ★☆☆ | Start Lab |
| 186 | 📖 Exploring Johnson-Lindenstrauss Lemma with Random ... | ★☆☆ | Start Lab |
| 187 | 📖 Principal Component Analysis with Kernel PCA | ★☆☆ | Start Lab |
| 188 | 📖 Outlier Detection With Scikit-Learn | ★☆☆ | Start Lab |
| 189 | 📖 Digit Dataset Analysis | ★☆☆ | Start Lab |
| 190 | 📖 Gaussian Mixture Model Covariances | ★☆☆ | Start Lab |
| 191 | 📖 Gaussian Mixture Model Selection | ★☆☆ | Start Lab |
| 192 | 📖 Plot Grid Search Digits | ★☆☆ | Start Lab |
| 193 | 📖 Multiclass ROC Evaluation With Scikit-Learn | ★☆☆ | Start Lab |
| 194 | 📖 Semi-Supervised Classifiers on the Iris Dataset | ★☆☆ | Start Lab |
| 195 | 📖 Gradient Boosting Out-of-Bag Estimates | ★☆☆ | Start Lab |
| 196 | 📖 Text Feature Extraction and Evaluation | ★☆☆ | Start Lab |
| 197 | 📖 Image Denoising With Kernel PCA | ★☆☆ | Start Lab |
| 198 | 📖 Anomaly Detection With Isolation Forest | ★☆☆ | Start Lab |
| 199 | 📖 Hashing Feature Transformation | ★☆☆ | Start Lab |
| 200 | 📖 Explicit Feature Map Approximation for RBF Kernels | ★☆☆ | Start Lab |
| 201 | 📖 Plotting Classification Probability | ★☆☆ | Start Lab |
| 202 | 📖 Probability Calibration for 3-Class Classification | ★☆☆ | Start Lab |
| 203 | 📖 Plot Compare GPR KRR | ★☆☆ | Start Lab |
| 204 | 📖 Feature Transformations With Ensembles of Trees | ★☆☆ | Start Lab |
| 205 | 📖 Feature Importance With Random Forest | ★☆☆ | Start Lab |
| 206 | 📖 Multi-Layer Perceptron Regularization | ★☆☆ | Start Lab |
| 207 | 📖 Discrete Versus Real AdaBoost | ★☆☆ | Start Lab |
| 208 | 📖 Scikit-Learn MLPClassifier: Stochastic Learning St... | ★☆☆ | Start Lab |
| 209 | 📖 Kernel Density Estimation | ★☆☆ | Start Lab |
| 210 | 📖 Plot Pca vs Lda | ★☆☆ | Start Lab |
| 211 | 📖 Univariate Feature Selection | ★☆☆ | Start Lab |
| 212 | 📖 Early Stopping of Stochastic Gradient Descent | ★☆☆ | Start Lab |
| 213 | 📖 Feature Selection for SVC on Iris Dataset | ★☆☆ | Start Lab |
| 214 | 📖 Approximate Nearest Neighbors in TSNE | ★☆☆ | Start Lab |
| 215 | 📖 K-Means Clustering on Handwritten Digits | ★☆☆ | Start Lab |
| 216 | 📖 Linear Discriminant Analysis for Classification | ★☆☆ | Start Lab |
| 217 | 📖 Plot Sgdocsvm vs Ocsvm | ★☆☆ | Start Lab |
| 218 | 📖 Plot Kernel Ridge Regression | ★☆☆ | Start Lab |
| 219 | 📖 Polynomial Kernel Approximation With Scikit-Learn | ★☆☆ | Start Lab |
| 220 | 📖 Scikit-Learn Visualization API | ★☆☆ | Start Lab |
| 221 | 📖 Plot Random Forest Regression Multioutput | ★☆☆ | Start Lab |
| 222 | 📖 Multiclass Sparse Logistic Regression | ★☆☆ | Start Lab |
| 223 | 📖 Creating Visualizations With Display Objects | ★☆☆ | Start Lab |
| 224 | 📖 Iris Flower Classification using Voting Classifier | ★☆☆ | Start Lab |
| 225 | 📖 Comparison Between Grid Search and Successive Halv... | ★☆☆ | Start Lab |
| 226 | 📖 Plot Nca Classification | ★☆☆ | Start Lab |
| 227 | 📖 Transforming Target for Linear Regression | ★☆☆ | Start Lab |
| 228 | 📖 Successive Halving Iterations | ★☆☆ | Start Lab |
| 229 | 📖 Plot Digits Pipe | ★☆☆ | Start Lab |
| 230 | 📖 Scikit-Learn Estimators and Pipelines | ★☆☆ | Start Lab |
| 231 | 📖 Classify Handwritten Digits with MLP Classifier | ★☆☆ | Start Lab |
| 232 | 📖 Gradient Boosting With Categorical Features | ★☆☆ | Start Lab |
| 233 | 📖 Plot Pca vs Fa Model Selection | ★☆☆ | Start Lab |
| 234 | 📖 Building Machine Learning Pipelines with Scikit-Le... | ★☆☆ | Start Lab |
| 235 | 📖 Balance Model Complexity and Cross-Validated Score | ★☆☆ | Start Lab |
| 236 | 📖 Text Document Classification | ★☆☆ | Start Lab |
| 237 | 📖 Digit Classification With RBM Features | ★☆☆ | Start Lab |
| 238 | 📖 Comparison of Calibration of Classifiers | ★☆☆ | Start Lab |
| 239 | 📖 Face Recognition With Eigenfaces and SVMs | ★☆☆ | Start Lab |
| 240 | 📖 Concatenating Multiple Feature Extraction Methods | ★☆☆ | Start Lab |
| 241 | 📖 Detection Error Tradeoff Curve | ★☆☆ | Start Lab |
| 242 | 📖 Dimensionality Reduction With Pipeline and GridSea... | ★☆☆ | Start Lab |
| 243 | 📖 Effect of Varying Threshold for Self-Training | ★☆☆ | Start Lab |
| 244 | 📖 Probability Calibration Curves | ★☆☆ | Start Lab |
| 245 | 📖 Class Likelihood Ratios to Measure Classification ... | ★☆☆ | Start Lab |
| 246 | 📖 Plot PCR vs PLS | ★☆☆ | Start Lab |
| 247 | 📖 Multi-Label Document Classification | ★☆☆ | Start Lab |
| 248 | 📖 Column Transformer With Mixed Types | ★☆☆ | Start Lab |
| 249 | 📖 Using Set_output API | ★☆☆ | Start Lab |
| 250 | 📖 Anomaly Detection Algorithms Comparison | ★☆☆ | Start Lab |
| 251 | 📖 Multiclass and Multioutput Algorithms | ★☆☆ | Start Lab |
| 252 | 📖 Precision-Recall Metric for Imbalanced Classificat... | ★☆☆ | Start Lab |
| 253 | 📖 Semi-Supervised Text Classification | ★☆☆ | Start Lab |
| 254 | 📖 Impute Missing Data | ★☆☆ | Start Lab |
| 255 | 📖 Feature Discretization for Classification | ★☆☆ | Start Lab |
| 256 | 📖 Pipelines and Composite Estimators | ★☆☆ | Start Lab |
| 257 | 📖 Feature Scaling in Machine Learning | ★☆☆ | Start Lab |
| 258 | 📖 Constructing Scikit-Learn Pipelines | ★☆☆ | Start Lab |
| 259 | 📖 Scikit-Learn Iterative Imputer | ★☆☆ | Start Lab |
| 260 | 📖 Manifold Learning on Handwritten Digits | ★☆☆ | Start Lab |
| 261 | 📖 Scikit-Learn Classifier Comparison | ★☆☆ | Start Lab |
## More
- 🔗 [scikit-learn Programming Courses](https://github.com/labex-labs/awesome-programming-courses)
- 🔗 [scikit-learn Programming Projects](https://github.com/labex-labs/awesome-programming-projects)
- 🔗 [scikit-learn Free Tutorials](https://github.com/labex-labs/sklearn-free-tutorials)