Grid Search in scikit-learn
The performance of our Machine Learning model is largely based on the hyperparameter values for the model. Hence, hyperparameter tuning
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Author: Pallavi Pandey
Cross-Validation in scikit-learn
Cross-validation is a statistical method used in Machine Learning for estimating the performance of models. It is very important to
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Feature Scaling: MinMax, Standard and Robust Scaler
Feature Scaling is performed during the Data Preprocessing step. Also known as normalization, it is a method that is used
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XGBoost Algorithm using Python
XGBoost is one of the most popular boosting algorithms. It is well known to arrive at better solutions as compared
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Outlier Detection using Isolation Forests
For a dataset, an outlier is a data point that behaves differently from the other data points. Outliers cause huge
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Introduction to Ensemble Techniques: Bagging and Boosting
Ensemble Techniques are Machine Learning techniques that combine predictions from several models to give an optimal model. Several models are
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Dimensionality Reduction using tSNE
tSNE stands for t-distributed Stochastic Neighbor Embedding. It is a dimensionality reduction technique and is extremely useful for visualizing datasets
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Dimensionality Reduction using PCA
Dimensionality refers to the number of input variables (or features) of the dataset. Data with a large number of features
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Evaluating Clustering Methods
Predicting optimal clusters is of utmost importance in Cluster Analysis. For a given data, we need to evaluate which Clustering
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Spectral Clustering
Spectral Clustering is gaining a lot of popularity in recent times, owing to its simple implementation and the fact that
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