- CRISP-DM
- SEMMA
- Anaconda/miniconda
- Streamlit
- Jupyter Notebooks
- MLflow
- Weights & Biases
- comet.ml
- Matplotlib
- Seaborn
- Plotly
- PivotTable.js
-Accuracy
– Precision (P)
– Recall (R)
– F1 rating (F1)
– Area under the ROC (Receiver Operating Characteristic) curve
– Log loss
– Precision at k (P@k)
– Average precision at k (AP@k)
– Mean average precision at k (MAP@k)
– Mean absolute error (MAE)
– Mean squared error (MSE)
– Root mean squared error (RMSE)
– Root mean squared logarithmic error (RMSLE)
– Mean percentage error (MPE)
– Mean absolute percentage error (MAPE)
– R2
- Hugging Face models
- TensorFlow hub
- PyTorch hub
- Grid search
- Random search
- Coarse-to-fine search
- Bayesian
optimisation with Gaussian process. - Skopt
- Hyperopt
- W&B Hyper-parameter Sweep
- Ray Tune
- Optuna
- Z-Rating
- IQR method
- Percentile method
- Residual vs Leverage plot
- Random under sampler
- Random over sampler
- SMOTE
- Pandas-Profiling
- Sweetviz
- Autoviz
- D-Tale
- k-fold cross-validation
- stratified k-fold cross-validation
- hold-out based validation
- leave-one-out cross-validation
- group k-fold cross-validation
- Pandas date-time feature
- Aggregates in pandas
- tsfresh library (for time series data)
- Polynomial features(Sk-learn)
- Binning (using panda’s cut function)
- Transformations(log,reciprocal,power,box-cox,etc.)
- Variance threshold & Variance inflation factor(VIF)
- Feature coefficient & feature importance
- Pearson correlation
- chi-square
- F-test
- Mutual information
- Greedy feature selection
- RFE(recursive feature elimination)
- SelectFromModel(from Sklearn)
2.
- Vote classification
- Mixing /Stacking
- PCA(incremental,kernel,randomise)
- LLE(Locally Linear Embedding)
- Multidimensional Scaling (MDS)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
- Isomap
- Singular Value Decomposition (SVD)
- OLS regression
- Polynomial regression
- Generalized Linear Model(GLM)
- curvefit(non-linear regression)
- Lasso & Ridge regression
- ElasticNet regression
- Logistic regression
- Lazy classifier
- Softmax Regression
- SVM
- KNN
- Random forest
- Adaboost
- XGboost
- K-means
- Agglomerative clustering
- Spectral clustering
- Mean shift
- Density-based Spatial Clustering of Applications with Noise
(DBSCAN) - K-medoids
- Kernel density estimation (KDE) &Bandwidth selection criteria
- Anomaly detection with Isolation Forests
And that’s a wrap! 📄
I hope this text helped you in giving an overall idea of various ML approaches.When you like this then 💬 Let me know within the comments .Share it with friends!! This things take numerous effort and time to be done so the feedback may be very appreciated! ❤️
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