## 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

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Grid Search in scikit-learn

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Cross-Validation in scikit-learn

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Feature Scaling: MinMax, Standard and Robust Scaler

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XGBoost Algorithm using Python

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Outlier Detection using Isolation Forests

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Introduction to Ensemble Techniques: Bagging and Boosting

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Dimensionality Reduction using tSNE

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Dimensionality Reduction using PCA

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Evaluating Clustering Methods

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Spectral Clustering

The performance of our Machine Learning model is largely based on the hyperparameter values for the model. Hence, hyperparameter tuning

Read moreCross-validation is a statistical method used in Machine Learning for estimating the performance of models. It is very important to

Read moreFeature Scaling is performed during the Data Preprocessing step. Also known as normalization, it is a method that is used

Read moreXGBoost is one of the most popular boosting algorithms. It is well known to arrive at better solutions as compared

Read moreFor a dataset, an outlier is a data point that behaves differently from the other data points. Outliers cause huge

Read moreEnsemble Techniques are Machine Learning techniques that combine predictions from several models to give an optimal model. Several models are

Read moretSNE stands for t-distributed Stochastic Neighbor Embedding. It is a dimensionality reduction technique and is extremely useful for visualizing datasets

Read moreDimensionality refers to the number of input variables (or features) of the dataset. Data with a large number of features

Read morePredicting optimal clusters is of utmost importance in Cluster Analysis. For a given data, we need to evaluate which Clustering

Read moreSpectral Clustering is gaining a lot of popularity in recent times, owing to its simple implementation and the fact that

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