Hits: 29 How to Save and Restore scikit learn Models On many occasions, while working with the scikit-learn library, you’ll need to save your prediction models to file, and then restore them in order to reuse your previous work to: test your model on new data, compare multiple models, or anything else. This saving procedure is also …
Hits: 6 Find Support Vectors Preliminaries /* Load libraries */ from sklearn.svm import SVC from sklearn import datasets from sklearn.preprocessing import StandardScaler import numpy as np Load Iris Flower Dataset /* Load data with only two classes */ iris = datasets.load_iris() X = iris.data[:100,:] y = iris.target[:100] Standardize Features /* Standarize features */ scaler = …
Hits: 9 K-Nearest Neighbors Classification Preliminaries import pandas as pd from sklearn import neighbors import numpy as np %matplotlib inline import seaborn Create Dataset Here we create three variables, test_1 and test_2 are our independent variables, ‘outcome’ is our dependent variable. We will use this data to train our learner. training_data = pd.DataFrame() training_data[‘test_1’] = [0.3051,0.4949,0.6974,0.3769,0.2231,0.341,0.4436,0.5897,0.6308,0.5] training_data[‘test_2’] = [0.5846,0.2654,0.2615,0.4538,0.4615,0.8308,0.4962,0.3269,0.5346,0.6731] …
Hits: 13 Handle Imbalanced Classes In Random Forest Preliminaries /* Load libraries */ from sklearn.ensemble import RandomForestClassifier import numpy as np from sklearn import datasets Load Iris Flower Dataset /* Load data */ iris = datasets.load_iris() X = iris.data y = iris.target Adjust Iris Dataset To Make Classes Imbalanced /* Make class highly imbalanced by …
Hits: 5 Feature Selection Using Random Forest Often in data science we have hundreds or even millions of features and we want a way to create a model that only includes the most important features. This has three benefits. First, we make our model more simple to interpret. Second, we can reduce the variance of …
Hits: 7 Feature Importance Preliminaries /* Load libraries */ from sklearn.ensemble import RandomForestClassifier from sklearn import datasets import numpy as np import matplotlib.pyplot as plt Load Iris Flower Dataset /* Load data */ iris = datasets.load_iris() X = iris.data y = iris.target Train A Decision Tree Model /* Create decision tree classifer object */ clf …
Hits: 5 Handling Imbalanced Classes In Logistic Regression Preliminaries /* Load libraries */ from sklearn.linear_model import LogisticRegression from sklearn import datasets from sklearn.preprocessing import StandardScaler import numpy as np Load Iris Flower Dataset /* Load data */ iris = datasets.load_iris() X = iris.data y = iris.target Make Classes Imbalanced /* Make class highly imbalanced by …
Hits: 17 Hyperparameter Tuning Using Random Search Preliminaries /* Load libraries */ from scipy.stats import uniform from sklearn import linear_model, datasets from sklearn.model_selection import RandomizedSearchCV Load Iris Dataset /* Load data */ iris = datasets.load_iris() X = iris.data y = iris.target Create Logistic Regression /* Create logistic regression */ logistic = linear_model.LogisticRegression() Create Hyperparameter Search …
Hits: 12 Generate Text Reports On Performance Preliminaries /* Load libraries /* from sklearn import datasets from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report Load Iris Flower Data /* Load data */ iris = datasets.load_iris() /* Create feature matrix */ X = iris.data /* Create target vector */ y = iris.target …
Hits: 9 Rescale A Feature Preliminaries from sklearn import preprocessing import numpy as np Create Feature x = np.array([[-500.5], [-100.1], [0], [100.1], [900.9]]) Rescale Feature Using Min-Max minmax_scale = preprocessing.MinMaxScaler(feature_range=(0, 1)) x_scale = minmax_scale.fit_transform(x) x_scale array([[ 0. ], [ 0.28571429], [ 0.35714286], [ 0.42857143], [ 1. ]]) Python Example for Beginners Special 95% discount …
Hits: 13 Preprocessing Iris Data Preliminaries from sklearn import datasets import numpy as np from sklearn.cross_validation import train_test_split from sklearn.preprocessing import StandardScaler Load Data iris = datasets.load_iris() X = iris.data y = iris.target Split Data For Cross Validation X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) Standardize Feature Data sc = StandardScaler() sc.fit(X_train) X_train_std …
Hits: 193 In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in R programming: Machine Learning in Python | Data Science for Beginners | TuriCreate | IRIS | AutoML Classification. What should I learn from this Applied …
Hits: 187 In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in R programming: ML Classification in Python | H2O.ai | Grid Search CV | Data Science Tutorials | Pandas | Jupyter Notebooks. What should I learn from …
Hits: 235 In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in R programming: ML Classification in Python | Data Science Tutorials | Tensorflow | Keras | IRIS | Deep Learning. What should I learn from this Applied Machine …
Hits: 108 In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in R programming: ML Classification in Python | Data Science Tutorials | XgBoost | MCCV | Pandas | IRIS Dataset. What should I learn from this Applied Machine …
Hits: 258 In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in R programming: ML Classification in Python | XGBoost | Grid Search CV | Data Science Tutorials | IRIS Dataset | Pandas. What should I learn from …
Hits: 94 In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in R programming: ML Classification in Python | Monte Carlo CV | GBM Algo | IRIS | Data Science Tutorials | Pandas. What should I learn from …
Machine Learning Classification in Python | Gradient Boosting | GSCV | IRIS | Data Science Tutorials
Hits: 240 In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in R programming: Machine Learning Classification in Python | Gradient Boosting | GSCV | IRIS | Data Science Tutorials. What should I learn from this Applied Machine …