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1. Top results related to model.predict sklearn

   • model.fit vs model.predict - differences & usage in sklearn

     Top Answer

     Answered Jun 12, 2019 · 5 votes

     Q 1.1 : Why do we use validation data over test data? (in above scenario)

     train_x, val_x, train_y, val_y = train_test_split(X, y,test_size=0.3)-

     First of all, the terms validation set and test set are very loosely used in many tutorials and sometimes interchangeably. It is quite possible to call the above val_x, val_y as test_x, test_y

     Q 1.2 :Why not all, train, val, and test? (why the split?)

     All our machine learning algorithms are going to be used on some real-world data (refer actual test data). However after devising an algorithm we want to "test" how well it performs, what is its accuracy, etc.

     Actually we currently don't have the real world data! Right?

     But what do we have? The train data! so we cleverly put aside a portion of it (splitting) for later testing the algorithm. The test data is used to evaluate the perform once the model is ready.

     model = DecisionTreeRegressor() model.fit(train_x, train_y)val_predictions = model.predict(val_x) # contains y values predicted by the modelscore = model.score(val_x, val_y) # evaluates predicted y against actual y of test dataprint(score)-

     Q 2. : For the model.predict() statement, why do we put val_x in there? Don't we want to predict val_y?

     Absolutely right we want to predict val_y, but the model needs val_x to predict y. That's exactly what we are passing as argument to the predict function.

     I understand it might be confusing to read model predict val_x.

     So the better way is to interpret it, as model could you u please predict

     from val_x, and return predicted_y.

     I say predicted_yand not val_y because, both won't be exactly similar. How much they differ? That is what given by score.

     Some Terminologies

     • Data Set : Data in hand. It is this data that gets divided later
     • Train Set : It is part of Data Set from which our model learns. Usually large, about 70-80%. Commmonly denoted by train_x and train_y.
     • Test Set : Part of Data Set that we set aside to evaluate the performance of model. This "tests" the model hence the name. Denoted by test_x and test_y.
     • Validation Set : If we want unbiased estimates of accuracy during the learning process, we use another split of Data Set. Usually to find hyperparamters etc. Typically to Pick best-performing algorithm (NB vs DT vs..)Fine-tune parameters (Tree depth, k in kNN, c in SVM)

     Q 1.3 : What is the use case for which combination to use?

     You will always have train & test, or all three. However in your case the test is just named as val.

     BONUS Question : In many tutorials I saw how StandardScalers are applied. However, in this tutorial it doesnt appear as such or did some other function already scale it without having to explicitly state it?

     It all depends on your data. If the data is pre-processed and all scaled properly then StandardScalers need not be applied. This particular tutorial just implies that data is already normalised accordingly.

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     1/5

   • sklearn how to use saved model to predict new data

     Top Answer

     Answered Jun 01, 2020 · 3 votes

     You need to save the model AND the tfidf transformer. You can either save them separately, or create a pipeline of the two and save the pipeline (this is the preferred option).

     Example:

     from sklearn.linear_model import LogisticRegressionfrom sklearn.feature_extraction.text import TfidfVectorizerfrom sklearn.pipeline import Pipelineimport pickleTfidf = TfidfVectorizer()LR = LogisticRegression()pipe = Pipeline([("Tfidf", Tfidf), ("LR", LR)])pipe.fit(X, y)with open('pipe.pickle', 'wb') as picklefile: pickle.dump(pipe, picklefile)

     You can then load the whole pipeline which upon predict will first apply the vectorizer and then pass it to the model:

     with open('pipe.pickle', 'rb') as picklefile: saved_pipe = pickle.load(picklefile)-saved_pipe.predict(X_test)

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     2/5

   • model.predict throws ValueError: expected shape=(None, 64, 64, 3), found shape=(None, 64, 3)

     Top Answer

     Answered Sep 04, 2022 · 0 votes

     The problem is that you are not specifying the batch dimension. Try this code:

     import tensorflow as tfimport numpy as np-PATCHSIZE = 64-# CNN modelmodel = tf.keras.models.Sequential([ # Convolutional layer. Learn 32 filters using a 3x3 kernel tf.keras.layers.Conv2D(32, (3, 3), activation="relu", input_shape=(PATCHSIZE, PATCHSIZE, 3)), #Max-pooling layer, using 2x2 pool size tf.keras.layers.MaxPooling2D(pool_size=(2, 2)), # Flatten units tf.keras.layers.Flatten(), # Add a hidden layer with dropout tf.keras.layers.Dense(128, activation="relu"), tf.keras.layers.Dropout(0.5), # Add an output layer with one output unit for a probability tf.keras.layers.Dense(1, activation="sigmoid")])model.compile( optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])training_patches = np.ones((1, PATCHSIZE, PATCHSIZE, 3))training_labels = np.ones((1, 1))model.fit(training_patches, training_labels, epochs=20)validation_patches = np.ones((1, PATCHSIZE, PATCHSIZE, 3))validation_labels = np.ones((1, 1))model.evaluate(validation_patches, validation_labels, verbose=2)

     If you have a single validation_patch, an easy way to add a dimension to an already existing numpy array is like this:

     my_array = np.ones((64, 64, 3))print(my_array.shape) # output: (64, 64, 3)my_array = np.expand_dims(my_array, axis=0)print(my_array.shape) # output: (1, 64, 64, 3)-

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     3/5

   • model.predict return values

     Top Answer

     Answered Jul 28, 2020 · 1 votes

     If I'm not mistaken model.predict returns numpy array for each corresponding prediction of the y_test. To interpret the values you could use evaluate method to find the accuracy of your model

     score = model.evaluate(x_test, y_test, verbose=0)print('Test loss:', score[0])print('Test accuracy:', score[1])

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     4/5

   • Model.fit() Validation Accuracy different than Model.predict()

     Top Answer

     Answered Nov 22, 2020 · 1 votes

     What fixed it for me was that I created another Image Data Generator variable

     test2_datagen = ImageDataGenerator(rescale = 1./255)-test2_set = test2_datagen.flow_from_directory('/content/drive/My Drive/data_sep/validate', target_size = (320, 320), batch_size = 32, class_mode = 'binary', Shuffle = False)

     But as you can see I set Shuffle = False

     . I am posting this answer in case anyone has the same problem. So I used test2_set for for the prediction.

     Check all answers on Stack Overflow

     5/5

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2. machinelearningmastery.com › make-predictionsHow to Make Predictions with scikit-learn ...

   machinelearningmastery.com › make-predictions

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   Apr 5, 2018 · predictions = model.predict(X_validation) print(accuracy_score(Y_validation, predictions)) print(confusion_matrix(Y_validation, predictions)) print(classification_report(Y_validation, predictions)) prediction_df = pd.DataFrame(predictions) prediction_df.to_csv(‘result.csv’)

   • How to Train a Final Machine Learning Model

     What is a Final Model? A final machine learning model is a...

   • Save and Load Machine Learning Models in Python With Scikit-Learn

     Finding an accurate machine learning model is not the end of...

   • How to Make Predictions with Keras

     Classification Predictions. Regression Predictions. 1....

3. www.sharpsightlabs.com › blog › sklearn-predictHow to Use the Sklearn Predict Method - Sharp Sight

   www.sharpsightlabs.com › blog › sklearn-predict

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   • A Quick Introduction to sklearn Predict
   • The Syntax of The sklearn Predict Method
   • Example: How to Use sklearn Predict
   • For More Machine Learning Tutorials, Sign Up For Our Email List

   To understand what the Sklearn predict method does, you need to understand the overall machine learning process. Creating and using a machine learning model has several phases, but we can break it down into two major steps: 1. train the model 2. use the model Of course, it’s a little more complicated than that. We often need to evaluate the model, ...

   See full list on sharpsightlabs.com

   Now that we’ve discussed what the Sklearn predict method does, let’s look at the syntax. One reminder: this syntax explanation here assumes that you’ve imported scikit-learn and that you’ve initialized a model, such as LinearRegression, RandomForestRegressor, etc.

   See full list on sharpsightlabs.com

   Now that we’ve examined how the syntax works, let’s work through an example of how to use Sklearn predict. In this example, I’ll show you how to use a machine learning model to make a “prediction.” This of course assumes that we’ve trained the model, so we’ll need to train the model first. All that being said, there are several steps to this exampl...

   See full list on sharpsightlabs.com

   In this tutorial, I’ve shown you how to use the Sklearn predict method. But if you want to master machine learning in Python, there’s a lot more to learn. That said, if you want to master scikit learn and machine learning in Python, then sign up for our email list. When you sign up, you’ll get free tutorials on: 1. Scikit learn 2. Machine learning ...

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4. People also ask

   What does the sklearn predict method do?

   • That’s essentially what the predict() method does. Once the model is trained, we can use predict() to predict an output value, on the basis of input values. Now that we’ve discussed what the Sklearn predict method does, let’s look at the syntax.

   How to Use the Sklearn Predict Method - Sharp Sight

   www.sharpsightlabs.com/blog/sklearn-predict/

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   How to predict a class in scikit-learn?

   • We can predict the class for new data instances using our finalized classification model in scikit-learn using the predict () function. For example, we have one or more data instances in an array called Xnew. This can be passed to the predict () function on our model in order to predict the class values for each instance in the array.

   How to Make Predictions with scikit-learn

   machinelearningmastery.com/make-predictions-scikit-learn/

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   How do I train a model to make predictions?

   • Before you can make predictions, you must train a final model. You may have trained models using k-fold cross validation or train/test splits of your data. This was done in order to give you an estimate of the skill of the model on out-of-sample data, e.g. new data. These models have served their purpose and can now be discarded.

   How to Make Predictions with scikit-learn

   machinelearningmastery.com/make-predictions-scikit-learn/

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   How do machine learning models make predictions?

   • Frequently, we use the model to make predictions about outputs, given new inputs. Once trained, many machine learning models are primarily used to make predictions. We predict an output value, given a set of input values. So for example, let’s say that we have a model that predicts housing prices.

   How to Use the Sklearn Predict Method - Sharp Sight

   www.sharpsightlabs.com/blog/sklearn-predict/

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5. stackoverflow.com › questions › 19984957python - scikit-learn .predict() default threshold - Stack ...

   stackoverflow.com › questions › 19984957

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   6 Answers. Sorted by: 87. The threshold can be set using clf.predict_proba() for example: from sklearn.tree import DecisionTreeClassifier. clf = DecisionTreeClassifier(random_state = 2) clf.fit(X_train,y_train) # y_pred = clf.predict(X_test) # default threshold is 0.5.

   Code sample

   >>> X = [[1, 0], [1, 0], [0, 1]]

   >>> y = [0, 0, 1]

   >>> MultinomialNB().fit(X,y).predict([1,1])

   array([0])

   >>> MultinomialNB(class_prior=[.1, .9]).fit(X,y).predict([1,1])...

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6. scikit-learn.org › stable › tutorialModel selection: choosing estimators and their parameters

   scikit-learn.org › stable › tutorial

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   Model selection: choosing estimators and their parameters # Score, and cross-validated scores # As we have seen, every estimator exposes a score method that can judge the quality of the fit (or the prediction) on new data. Bigger is better.

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8. www.activestate.com › resources › quick-readsHow to make predictions with Scikit-Learn - ActiveState

   www.activestate.com › resources › quick-reads

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   Apr 26, 2021 · model = LinearRegression(fit_intercept=True) model.fit(x[:, np.newaxis], y) xfit = np.linspace(0, 10, 1000) yfit = model.predict(xfit[:, np.newaxis]) # Plot the estimated linear regression line with matplotlib: . plt.scatter(x, y) plt.plot(xfit, yfit); plt.show() Figure 1. Estimated Linear Regression Line. Decision Tree Example.

9. medium.com › @preethi_prakash › mastering-machineMastering Machine Learning Predictions in Scikit-Learn - Medium

   medium.com › @preethi_prakash › mastering-machine

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   May 13, 2023 · In machine learning, the predict and predict_proba, predict_log_proba, and decision_function methods are used to make predictions based on a trained model. ‘predict’ method. The predict...